research supports the idea that attitudes predict

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5.1 Exploring Attitudes

Learning objectives.

• Define the concept of attitude and explain why it is of such interest to social psychologists.
• Review the variables that determine attitude strength.
• Outline the factors affect the strength of the attitude-behavior relationship.

Although we might use the term in a different way in our everyday life (“Hey, he’s really got an attitude !”), social psychologists reserve the term attitude to refer to our relatively enduring evaluation of something , where the something is called the attitude object . The attitude object might be a person, a product, or a social group (Albarracín, Johnson, & Zanna, 2005; Wood, 2000). In this section we will consider the nature and strength of attitudes and the conditions under which attitudes best predict our behaviors.

Attitudes Are Evaluations

When we say that attitudes are evaluations, we mean that they involve a preference for or against the attitude object, as commonly expressed in such terms as prefer , like , dislike , hate , and love . When we express our attitudes—for instance, when we say, “I love Cheerios,” “I hate snakes,” “I’m crazy about Bill,” or “I like Italians”—we are expressing the relationship (either positive or negative) between the self and an attitude object. Statements such as these make it clear that attitudes are an important part of the self-concept—attitudes tie the self-concept to the attitude object, and so our attitudes are an essential part of “us.”

Every human being holds thousands of attitudes, including those about family and friends, political parties and political figures, abortion rights and terrorism, preferences for music, and much more. Each of our attitudes has its own unique characteristics, and no two attitudes come to us or influence us in quite the same way. Research has found that some of our attitudes are inherited, at least in part, via genetic transmission from our parents (Olson, Vernon, Harris, & Jang, 2001). Other attitudes are learned mostly through direct and indirect experiences with the attitude objects (De Houwer, Thomas, & Baeyens, 2001). We may like to ride roller coasters in part because our genetic code has given us a thrill-loving personality and in part because we’ve had some really great times on roller coasters in the past. Still other attitudes are learned via the media (Hargreaves & Tiggemann, 2003; Levina, Waldo, & Fitzgerald, 2000) or through our interactions with friends (Poteat, 2007). Some of our attitudes are shared by others (most of us like sugar, fear snakes, and are disgusted by cockroaches), whereas other attitudes—such as our preferences for different styles of music or art—are more individualized.

Table 5.1 “Heritability of Some Attitudes” shows some of the attitudes that have been found to be the most highly heritable (i.e. most strongly determined by genetic variation among people). These attitudes form earlier and are stronger and more resistant to change than others (Bourgeois, 2002), although it is not yet known why some attitudes are more genetically determined than are others.

Table 5.1 Heritability of Some Attitudes

Our attitudes are made up of cognitive, affective, and behavioral components. Consider my own attitude toward chocolate ice cream, which is very positive and always has been, as far as I can remember.

In terms of affect:

In terms of behavior:

I frequently eat chocolate ice cream.

In terms of cognitions:

Chocolate ice cream has a smooth texture and a rich, strong taste.

My attitude toward chocolate ice cream is composed of affect, behavior, and cognition.

Although most attitudes are determined by cognition, affect, and behavior, there is nevertheless variability in this regard across people and across attitudes. Some attitudes are more likely to be based on beliefs, some more likely to be based on feelings, and some more likely to be based on behaviors. I would say that my attitude toward chocolate ice cream is in large part determined by affect—although I can describe its taste, mostly I just like it. My attitudes toward my Toyota Corolla and my home air conditioner, on the other hand, are more cognitive. I don’t really like them so much as I admire their positive features (the Toyota gets good gas mileage and the air conditioner keeps me cool on hot summer days). Still other of my attitudes are based more on behavior—I feel like I’ve learned to like my neighbors because I’ve done favors for them over the years (which they have returned) and these helpful behaviors on my part have, at least in part, led me to develop a positive attitude toward them.

Different people may hold attitudes toward the same attitude object for different reasons. Some people voted for Barack Obama in the 2008 elections because they like his policies (“he’s working for the middle class”; “he wants to increase automobile fuel efficiency”), whereas others voted for (or against) him because they just liked (or disliked) him. Although you might think that cognition would be more important in this regard, political scientists have shown that many voting decisions are made primarily on the basis of affect. Indeed, it is fair to say that the affective component of attitudes is generally the strongest and most important (Abelson, Kinder, Peters, & Fiske, 1981; Stangor, Sullivan, & Ford, 1991).

Human beings hold attitudes because they are useful. Particularly, our attitudes enable us to determine, often very quickly and effortlessly, which behaviors to engage in, which people to approach or avoid, and even which products to buy (Duckworth, Bargh, Garcia, & Chaiken, 2002; Maio & Olson, 2000). You can imagine that making quick decisions about what to avoid

snake = bad ⟶ run away

or to approach

blueberries = good ⟶ eat

has had substantial value in our evolutionary experience.

Because attitudes are evaluations, they can be assessed using any of the normal measuring techniques used by social psychologists (Banaji & Heiphetz, 2010). Attitudes are frequently assessed using self-report measures, but they can also be assessed more indirectly using measures of arousal and facial expressions (Mendes, 2008) as well as implicit measures of cognition, such as the Implicit Association Test (IAT) . Attitudes can also be seen in the brain by using neuroimaging techniques. This research has found that our attitudes, like most of our social knowledge, are stored primarily in the prefrontal cortex but that the amygdala is important in emotional attitudes, particularly those associated with fear (Cunningham, Raye, & Johnson, 2004; Cunningham & Zelazo, 2007; van den Bos, McClure, Harris, Fiske, & Cohen, 2007). Attitudes can be activated extremely quickly—often within one fifth of a second after we see an attitude object (Handy, Smilek, Geiger, Liu, & Schooler, 2010).

Some Attitudes Are Stronger Than Others

Some attitudes are more important than others, because they are more useful to us and thus have more impact on our daily lives. The importance of an attitude, as assessed by how quickly it comes to mind , is known as attitude strength (Fazio, 1990; Fazio, 1995; Krosnick & Petty, 1995). Some of our attitudes are strong attitudes, in the sense that we find them important, hold them with confidence, do not change them very much, and use them frequently to guide our actions. These strong attitudes may guide our actions completely out of our awareness (Ferguson, Bargh, & Nayak, 2005).

Other attitudes are weaker and have little influence on our actions. For instance, John Bargh and his colleagues (Bargh, Chaiken, Raymond, & Hymes, 1996) found that people could express attitudes toward nonsense words such as juvalamu (which people liked) and chakaka (which they did not like). The researchers also found that these attitudes were very weak. On the other hand, the heavy voter turnout for Barack Obama in the 2008 elections was probably because many of his supporters had strong positive attitudes about him.

Strong attitudes are attitudes that are more cognitively accessible—they come to mind quickly, regularly, and easily. We can easily measure attitude strength by assessing how quickly our attitudes are activated when we are exposed to the attitude object. If we can state our attitude quickly, without much thought, then it is a strong one. If we are unsure about our attitude and need to think about it for a while before stating our opinion, the attitude is weak.

Attitudes become stronger when we have direct positive or negative experiences with the attitude object, and particularly if those experiences have been in strong positive or negative contexts. Russell Fazio and his colleagues (Fazio, Powell, & Herr, 1983) had people either work on some puzzles or watch other people work on the same puzzles. Although the people who watched ended up either liking or disliking the puzzles as much as the people who actually worked on them, Fazio found that attitudes, as assessed by reaction time measures, were stronger (in the sense of being expressed quickly) for the people who had directly experienced the puzzles.

Because attitude strength is determined by cognitive accessibility, it is possible to make attitudes stronger by increasing the accessibility of the attitude. This can be done directly by having people think about, express, or discuss their attitudes with others. After people think about their attitudes, talk about them, or just say them out loud, the attitudes they have expressed become stronger (Downing, Judd, & Brauer, 1992; Tesser, Martin, & Mendolia, 1995). Because attitudes are linked to the self-concept, they also become stronger when they are activated along with the self-concept. When we are looking into a mirror or sitting in front of a TV camera, our attitudes are activated and we are then more likely to act on them (Beaman, Klentz, Diener, & Svanum, 1979).

Attitudes are also stronger when the ABCs of affect, behavior, and cognition all line up. As an example, many people’s attitude toward their own nation is universally positive. They have strong positive feelings about their country, many positive thoughts about it, and tend to engage in behaviors that support it. Other attitudes are less strong because the affective, cognitive, and behavioral components are each somewhat different (Thompson, Zanna, & Griffin, 1995). My affect toward chocolate ice cream is positive—I like it a lot. On the other hand, my cognitions are more negative—I know that eating too much ice cream can make me fat and that it is bad for my coronary arteries. And even though I love chocolate ice cream, I don’t eat some every time I get a chance. These inconsistencies among the components of my attitude make it less strong than it would be if all the components lined up together.

When Do Our Attitudes Guide Our Behavior?

Social psychologists (as well as advertisers, marketers, and politicians) are particularly interested in the behavioral aspect of attitudes. Because it is normal that the ABCs of our attitudes are at least somewhat consistent, our behavior tends to follow from our affect and cognition. If I determine that you have more positive cognitions about and more positive affect toward Cheerios than Frosted Flakes, then I will naturally predict (and probably be correct when I do so) that you’ll be more likely to buy Cheerios than Frosted Flakes when you go to the market. Furthermore, if I can do something to make your thoughts or feelings toward Frosted Flakes more positive, then your likelihood of buying that cereal instead of the other will also increase.

The principle of attitude consistency (that for any given attitude object, the ABCs of affect, behavior, and cognition are normally in line with each other ) thus predicts that our attitudes (for instance, as measured via a self-report measure) are likely to guide behavior . Supporting this idea, meta-analyses have found that there is a significant and substantial positive correlation among the different components of attitudes, and that attitudes expressed on self-report measures do predict behavior (Glasman & Albarracín, 2006).

Although there is generally consistency between attitudes and behavior, the relationship is stronger in certain situations, for certain people, and for certain attitudes (Wicker, 1969). The theory of planned behavior , developed by Martin Fishbein and Izek Ajzen (Ajzen, 1991; Fishbein & Ajzen, 1975), outlined many of the important variables that affected the attitude-behavior relationship, and some of these factors are summarized in the list that follows this paragraph. It may not surprise you to hear that attitudes that are strong, in the sense that they are expressed quickly and confidently, predict our behavior better than do weak attitudes (Fazio, Powell, & Williams, 1989; Glasman & Albarracín, 2006). For example, Farc and Sagarin (2009) found that people who could more quickly complete questionnaires about their attitudes toward the politicians George Bush and John Kerry were also more likely to vote for the candidate that they had more positive attitudes toward in the 2004 presidential elections. The relationship between the responses on the questionnaires and voting behavior was weaker for those who completed the items more slowly.

• When attitudes are strong, rather than weak
• When we have a strong intention to perform the behavior
• When the attitude and the behavior both occur in similar social situations
• When the same components of the attitude (either affect or cognition) are accessible when the attitude is assessed and when the behavior is performed
• When the attitudes are measured at a specific, rather than a general, level
• For low self-monitors (rather than for high self-monitors)

Attitudes only predict behaviors well under certain conditions and for some people. The preceding list summarizes the factors that create a strong attitude-behavior relationship.

People who have strong attitudes toward an attitude object are also likely to have strong intentions to act on their attitudes, and the intention to engage in an activity is a strong predictor of behavior (Fishbein & Ajzen, 1975). Imagine for a moment that your friend Sharina is trying to decide whether to recycle her used laptop batteries or just throw them away. We know that her attitude toward recycling is positive—she thinks she should do it—but we also know that recycling takes work. It’s much easier to just throw the batteries away. Only if Sharina has a strong attitude toward recycling will she then have the necessary strong intentions to engage in the behavior that will make her recycle her batteries even when it is difficult to do.

The match between the social situations in which the attitudes are expressed and the behaviors are engaged in also matters, such that there is a greater attitude-behavior correlation when the social situations match. Imagine for a minute the case of Magritte, a 16-year-old high school student. Magritte tells her parents that she hates the idea of smoking cigarettes. Magritte’s negative attitude toward smoking seems to be a strong one because she’s thought a lot about it—she believes that cigarettes are dirty, expensive, and unhealthy. But how sure are you that Magritte’s attitude will predict her behavior? Would you be willing to bet that she’d never try smoking when she’s out with her friends?

You can see that the problem here is that Magritte’s attitude is being expressed in one social situation (when she is with her parents) whereas the behavior (trying a cigarette) is going to occur in a very different social situation (when she is out with her friends). The relevant social norms are of course much different in the two situations. Magritte’s friends might be able to convince her to try smoking, despite her initial negative attitude, when they entice her with peer pressure. Behaviors are more likely to be consistent with attitudes when the social situation in which the behavior occurs is similar to the situation in which the attitude is expressed (Ajzen, 1991; LaPiere, 1936).

Research Focus

Attitude-Behavior Consistency

Another variable that has an important influence on attitude-behavior consistency is the current cognitive accessibility of the underlying affective and cognitive components of the attitude. For example, if we assess the attitude in a situation in which people are thinking primarily about the attitude object in cognitive terms, and yet the behavior is performed in a situation in which the affective components of the attitude are more accessible, then the attitude-behavior relationship will be weak. Wilson and Schooler (1991) showed a similar type of effect by first choosing attitudes that they expected would be primarily determined by affect—attitudes toward five different types of strawberry jam. Then they asked a sample of college students to taste each of the jams. While they were tasting, one-half of the participants were instructed to think about the cognitive aspects of their attitudes to these jams—that is, to focus on the reasons they held their attitudes, whereas the other half of the participants were not given these instructions. Then all the students completed measures of their attitudes toward each of the jams.

Wilson and his colleagues then assessed the extent to which the attitudes expressed by the students correlated with taste ratings of the five jams as indicated by experts at Consumer Reports . They found that the attitudes expressed by the students correlated significantly higher with the expert ratings for the participants who had not listed their cognitions first. Wilson and his colleagues argued that this occurred because our liking of jams is primarily affectively determined—we either like them or we don’t. And the students who simply rated the jams used their feelings to make their judgments. On the other hand, the students who were asked to list their thoughts about the jams had some extra information to use in making their judgments, but it was information that was not actually useful. Therefore, when these students used their thoughts about the jam to make the judgments, their judgments were less valid.

MacDonald, Zanna, and Fong (1996) showed male college students a video of two other college students, Mike and Rebecca, who were out on a date. However, according to random assignment to conditions, half of the men were shown the video while sober and the other half viewed the video after they had had several alcoholic drinks. In the video, Mike and Rebecca go to the campus bar and drink and dance. They then go to Rebecca’s room, where they end up kissing passionately. Mike says that he doesn’t have any condoms, but Rebecca says that she is on the pill.

At this point the film clip ends, and the male participants are asked about their likely behaviors if they had been Mike. Although all men indicated that having unprotected sex in this situation was foolish and irresponsible, the men who had been drinking alcohol were more likely to indicate that they would engage in sexual intercourse with Rebecca even without a condom. One interpretation of this study is that sexual behavior is determined by both cognitive factors (“I know that it is important to practice safe sex and so I should use a condom”) and affective factors (“sex is enjoyable, I don’t want to wait”). When the students were intoxicated at the time the behavior was to be performed, it seems likely the affective component of the attitude was a more important determinant of behavior than was the cognitive component.

One other type of “match” that has an important influence on the attitude-behavior relationship concerns how we measure the attitude and behavior. Attitudes predict behavior better when the attitude is measured at a level that is similar to the behavior to be predicted. Normally, the behavior is specific, so it is better to measure the attitude at a specific level too. For instance, if we measure cognitions at a very general level (“do you think it is important to use condoms?”; “are you a religious person?”) we will not be as successful at predicting actual behaviors as we will be if we ask the question more specifically, at the level of behavior we are interested in predicting (“do you think you will use a condom the next time you have sex?”; “how frequently do you expect to attend church in the next month?”). In general, more specific questions are better predictors of specific behaviors, and thus if we wish to accurately predict behaviors, we should remember to attempt to measure specific attitudes. One example of this principle is shown in Figure 5.1 “Predicting Behavior From Specific and Nonspecific Attitude Measures” . Davidson and Jaccard (1979) found that they were much better able to predict whether women actually used birth control when they assessed the attitude at a more specific level.

Figure 5.1 Predicting Behavior From Specific and Nonspecific Attitude Measures

Attitudes that are measured using more specific questions are more highly correlated with behavior than are attitudes measured using less specific questions. Data are from Davidson and Jaccard (1979).

Attitudes also predict behavior better for some people than for others. Self-monitoring refers to individual differences in the tendency to attend to social cues and to adjust one’s behavior to one’s social environment. To return to our example of Magritte, you might wonder whether she is the type of person who is likely to be persuaded by peer pressure because she is particularly concerned with being liked by others. If she is, then she’s probably more likely to want to fit in with whatever her friends are doing, and she might try a cigarette if her friends offer her one. On the other hand, if Magritte is not particularly concerned about following the social norms of her friends, then she’ll more likely be able to resist the persuasion. High self-monitors are those who tend to attempt to blend into the social situation in order to be liked; low self-monitors are those who are less likely to do so. You can see that, because they allow the social situation to influence their behaviors, the relationship between attitudes and behavior will be weaker for high self-monitors than it is for low self-monitors (Kraus, 1995).

Key Takeaways

• The term attitude refers to our relatively enduring evaluation of an attitude object.
• Our attitudes are inherited and also learned through direct and indirect experiences with the attitude objects.
• Some attitudes are more likely to be based on beliefs, some more likely to be based on feelings, and some more likely to be based on behaviors.
• Strong attitudes are important in the sense that we hold them with confidence, we do not change them very much, and we use them frequently to guide our actions.
• Although there is a general consistency between attitudes and behavior, the relationship is stronger in some situations than in others, for some measurements than for others, and for some people than for others.

Exercises and Critical Thinking

• Consider some of your attitudes toward people, products, or other attitude objects. Are your attitudes strong or weak? Are they determined more by affect or by cognition? How do the attitudes influence your behavior?
• Consider a time when you acted on your own attitudes and a time when you did not act on your own attitudes. What factors do you think determined the difference?

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Introducing an attitude-based approach to emotional intelligence

Jo maddocks.

1 Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, United Kingdom

2 Talogy, Guildford, United Kingdom

Associated Data

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Emotional intelligence (EI) was originally conceived as an ability, followed soon after by mixed, competency and trait theoretical models, broadly described as emotional efficacies (EE). Several models have attempted to integrate both approaches, with different views on whether EI and EE operate in sequence or parallel. One reason for this may be that EE constructs are given the same ontological status whether they represent underlying attitudes, such as self-regard, or behavioral competencies, such as assertiveness. In this paper, it is proposed that attitudes may predominantly act as underlying antecedents of ability-EI and behavioral-EE. Five benefits of this approach are drawn out that help to address some key concerns with current models and measures of EI and EE. First, the inclusion of implicit and explicit attitudes within integrated models of EI/EE would support the dual-processing of conscious and automated processes. From this, an attitude-based dual-processing framework for EI/EE is recommended. Second, the concept of Unconditional Positive Regard (UPR) for self and others, is identified as a potential attitude that may underpin the two core pillars of intrapersonal and interpersonal EI/EE. Third, UPR attitudes would provide an ethical basis for EI/EE that may support ethical and prosocial behavior. Fourth, UPR attitudes may differentiate between the optimal and sub-optimal elements of EI/EE. Fifth, an attitude-based approach to EI/EE may be more aligned with EI/EE being developmental than are the more static ability or trait-based models of EI/EE.

1. Introduction

Over the last 30 years, research into emotional intelligence (EI) has taken two distinct and contrary pathways dominated by a few prominent models. One examines EI as an ability ( Mayer and Salovey, 1993 ), measured as maximum performance. The other describes EI as a mixed array of affect-related traits ( Furnham and Petrides, 2003 ) or competencies ( Boyatzis and Sala, 2004 ), measured as typical performance. In recent years, there has been a wider recognition from theorists of both approaches that the term “emotional intelligence” (EI) may be reserved for the collection of cognitive and emotional processes aligned with the ability model, while emotion-related behaviors and traits are labeled something other than “intelligence” ( Mayer et al., 2008 ) such as “emotional and social competencies” ( Boyatzis, 2007 ), “affect-related personality” ( Hughes and Evans, 2018 . p. 5) and “trait emotional self-efficacy” ( Petrides et al., 2016 , p. 339). From here on in I employ the term “emotional efficacies” (EE), a label seen as acceptable across different sides of the ongoing debate that surround EI ( Dasborough et al., 2021 , p. 11).

This raises the question of how these two taxonomies of EI and EE are related. In a review of three decades of EI research, Keefer (2015) concludes that the ability and trait approaches to EI have become recognized as complementary rather than competing or contradictory, reflecting a shift toward more integrative approaches. These approaches take different forms. Some present EE traits and competencies as outcomes of EI ability ( Mikolajczak, 2009 ; Cherniss, 2010 ; Joseph et al., 2015 ; Boyatzis, 2016 ; Drigas and Papoutsi, 2018 ). Others consider EE traits to be antecedent to EI ability ( Seal and Andrews-Brown, 2010 ), while later models propose that some or all components of EE and EI may run in parallel ( Hughes and Evans, 2018 ; Vesely-Maillefer et al., 2018 ). Despite this move toward integration, other scholars lean toward their separation, noting the low correlation between EI and EE measures ( Brackett and Mayer, 2003 ; Warwick and Nettelbeck, 2004 ), with a foothold in either ability-EI ( Mayer et al., 2008 ; Dasborough et al., 2021 ) or the trait-EE camp ( Petrides, 2010 ). The integration of EI and EE models is gradually advancing as a field of theoretical research but is still to establish itself with a consistent and unifying approach. It is not yet agreed whether EE, or components of EE, are antecedent, parallel to, or consequences of ability-EI. This may in part be due to the potential overlap and breadth of constructs found across different models and measures of EE traits and EE competencies. For example, the Trait Emotional Intelligence Questionnaire (TEIQue; Petrides et al., 2007 ) has many similar scales labels (such as Trait Empathy, Trait Optimism, and Adaptability) to the Emotional and Social Competency Inventory (ESCI; Boyatzis, 2007 ; such as Empathy, Positive Outlook, and Adaptability). One reason it may be difficult to draw clear distinctions between EE traits and competencies is that they are given the same ontological status. For example, Bar-On (1997) , defines EQ (EE) as “a cross-section of interrelated emotional and social competencies, skills and facilitators…” but does not explicitly differentiate between these components. Facets such as Self Regard and Problem Solving, as measured by the Emotional Quotient Inventory (EQI; Bar-On, 1997 ) are given the same relative importance, with no attributional or causal relationship made between them. Similarly, facets from the TEIQue are collectively defined under broad descriptions such as “a constellation of emotional self-perceptions” ( Petrides et al., 2007 , p. 26) and “affective aspects of personality” ( Petrides et al., 2016 . p. 336), with no distinction made between emotional processes such as Emotional Regulation and dispositional traits such as Optimism. This reflects a wider concern about EE models being a “catch-all label” and a “grab bag” ( Murphy, 2006 ; Joseph and Newman, 2010 ) containing a diverse range of knowledge, skills, abilities, and other characteristics (KSAOs; Joseph et al., 2015 ).

One way forward would be to differentiate the ontological status of EE facets to distinguish those facets that are more profound and deterministic of EI-related outcomes from those that are specific behavioral manifestations of EI. For instance, the scale facet of Self Regard reflects an individual’s underlying self-concept that may manifest broadly in a person’s emotional, cognitive, and behavioral response to events ( Diener and Diener, 1995 ; Marsh and O’Mara, 2008 ). In this article, it is proposed that greater attention is given to an individual’s self-concept, in particular, their implicit and explicit attitudes as important determinants of both ability-EI and behavioral-EE.

2. Attitudes as a foundation for EI and EE

Attitude is frequently referenced in relation to EI and EE models, although this relationship is mostly described in broad and non-specific terms. In their integrated model of EI, Seal and Andrews-Brown (2010 , p. 147) suggest that “having the right attitude predisposes the use of certain skills, and that both attitude and skills are influenced by ability.” Similarly, Vesely-Maillefer et al. (2018) reference KSA (Knowledge, skills, attitude) taxonomy of Bloom (1976) , as analogous to their integrated model of EI. Cherniss and Boyatzis (2013) include a wide array of underlying elements to EI such as “motivation,” “unconscious dispositions,” and “values and philosophical foundations” as foundations of the ESCI model, and Keefer (2015 , p. 9) describes self-report scales of emotional competencies as an individual’s “EI self-concept.”

There is substantial evidence that attitudes initiate and influence cognitive and emotional processes. Ajzen (2007) defines an attitude as “a disposition to respond favorably or unfavorably to an object, person, institution or event” and most contemporary social psychologists agree that a defining characteristic of attitudes is they are evaluative, i.e., they elicit a cognitive and emotional response ( Osgood et al., 1957 ; Bem, 1970 ; Fishbein and Ajzen, 1975 ; Oskamp, 1991 ; Eagly and Chaiken, 1993 ). Neuroscientific evidence shows that attitudes, when stimulated, activate the emotional centers of the brain within the limbic system and amygdala ( Zald, 2003 ; Phelps, 2006 ), giving rise to an evaluative emotional response ( Peikoff, 1991 ), which then exerts influence on thought and behavior ( Fazio et al., 1986 ; Bargh et al., 1992 ; Baumeister et al., 2007 ). For example, attitudes influence where we focus our attention ( Gwinn and Krajbich, 2020 ), whether we interpret events through a positive or negative lens ( Cacioppo and Berntson, 1994 ), and how we choose to then respond to the event ( Fazio and Petty, 2008 ). Such evidence would suggest that attitudes may have an important influence on the perception, facilitation, understanding, and management of emotions, as represented in the ability and emotion processing models of EI.

The relationship between attitude and behavior is also well-established ( Ajzen and Fishbein, 1977 ; Fazio et al., 1986 ; Bargh et al., 1996 ). A large meta-analytic review found an average correlation of 0.52 between attitude-opinions and behavioral actions ( Glasman and Albarracín, 2006 ) concluding that attitudes influence future behaviors when they are easy to retrieve from memory and stable over time. The notion that individuals seek consistency and balance between attitude and behavior is also central to social and cognitive models of psychology ( Festinger, 1957 ; Heider, 1958 ; Bandura, 1986 ; Makin and Cox, 2004 ), such as self-consistency theory ( Korman, 1970 ) and self-verification theory ( Swann, 1992 ). Lindebaum (2009 , p. 233) asserts that “endeavors to stimulate individual’s EI should center upon attitudinal and perceptual changes before behavioral responses can change too.” Cognitive dissonance theory ( Festinger, 1957 ) demonstrates that an individual may feel emotionally compelled to align their behavior with their attitude or alter their attitude to fit with their behavior, to avoid feelings of dissonance and anxiety ( Cooper and Fazio, 1984 ). Wide reaching research into self efficacy ( Bandura, 1986 ) also provides strong conviction that self-belief, a close relative of attitude 1 greatly influences thought, motivations, and action. The close theoretical and empirical relationship between attitude and behavior is also widely applied within organizational settings to promote job satisfaction, organizational commitment, and personal development ( Miao et al., 2017 ). Given the depth and history of research that aligns attitudes to emotion, cognition, and behavior it is curious that the link between attitude, EI, and EE has not been made more explicit within current models.

On the premise that attitude is intrinsically linked with emotional processing and emotion-driven behavior, it follows that incorporating attitudes within existing integrated models of EI/EE may provide a causal link to EI ability and EE behaviors. This may take the form of attitudes as underpinning antecedents, which influence both ability-EI and behavioral-EE. In this paper, it is proposed that attitudes and an attitude-based approach to EI/EE present several potential advantages that help address concerns with current models of EI/EE. These include:

• (1) Greater understanding and separation between the conscious (implicit attitudes) and automated (explicit attitudes) dual-processing of EI/EE.
• (2) Foundations to the intrapersonal (internal attitudes) and interpersonal (external attitudes) pillars of EI/EE.
• (3) An ethical basis for EI/EE that promotes ethical and prosocial behavior.
• (4) Balance between the extremes of too much and too little EI/EE.
• (5) A platform that supports the developmental application of EI and EE.

These five benefits will be discussed with recommendations for an attitude-based dual-processing framework of EI/EE that differentiates the potential inputs (attitudes) and outputs (behavioral-EE) of EI (the ability).

3. Discussion

3.1. a dual-processing model of ei/ee.

A concern rarely considered by creators of EI models and measures is the differentiation between conscious and automated processes. Ybarra et al. (2014 , p. 96) claim that ability-EI models tend to focus on the conscious cognitive processing of emotions, i.e., awareness, understanding, and management of emotions, but ignore automatic unconscious processes, missing out on an important part of the EI puzzle. For example, the MSCEIT ability instrument ( Mayer et al., 1997 ), has been criticized for tapping into emotion-related knowledge rather than emotion-related ability. As Ashton-James (2003) explains, knowing what one should say, or how one should behave to sustain a relationship in a specific situation (conscious knowledge), does not mean that one will behave this way in practice. Fiori and Vesely-Maillefer (2018 , p. 37) describe this as “among the most compelling theoretical challenges we need to address” and argue that incorporating automatic processes into a model of EI is critical because a large portion of social and emotional life is regulated through the deployment of such processes ( Bargh and Chartrand, 1999 ; Kahneman, 2011 ). Dual-processing models of EI have been advocated by a few researchers ( Evans, 2008 ; Fiori, 2009 ; Ybarra et al., 2014 ) and are consistent with wider dual-processing theories of emotion ( Baumeister et al., 2007 ). Fiori (2009) recommends a dual-processing framework of ability-EI that include both crystalized EI (emotion knowledge) and fluid EI (emotional information processing), suggesting that ability-EI measures that focus on crystalized EI may be more suited to predicting “effortful and consciously accessible emotional behavior,” and that measures of fluid EI or emotion information processing may account more for “spontaneous and unintentional behavior” i.e., automaticity. More recently, this has been developed into an integrated model of EI ( Vesely-Maillefer et al., 2018 ) with three components that interact and operate simultaneously (PAT): Processing of emotional information (fluid EI), Ability (crystalized EI), and Traits.

As with ability-EI models and measures, the behavioral-EE approaches make little separation between automatic, habitual behaviors and more deliberate, conscious behavioral actions. The majority of EE trait and competency measures are self-report, which require conscious and accurate self-awareness of one’s behavioral and emotional patterns. However, EE traits and competencies are typically dispositional, habitual, and skill-based concepts, which are likely to be more automated and often unconscious processes.

It is proposed that this important distinction between automatic and conscious processing is incorporated into models of ability-EI and behavioral-EE and that attitudes could form a basis for such an approach. Attitude theory lends itself well to a dual-process approach as a clear distinction is made between explicit attitudes, which operate at a conscious level, are deliberately formed, and are easy to self-report, and implicit attitudes, which operate at the unconscious level, are involuntarily formed, and are typically unknown to us ( Bassili and Brown, 2005 ). From their review of the literature, Bohner and Dickel (2011 , p. 412) comment that “Research on attitudes as precursors of behavior shows that implicit measures of attitude predict spontaneous, less controllable behavior, whereas explicit measures of attitude predict deliberative, more controlled behavior.” One reason why attitudes may have been neglected from EI research is that they are often implicit, and therefore difficult to identify and not easily available through self-report. Implicit attitudes may be seen as templates, patterns ( Griffin and Tyrell, 2001 ), or simulations ( Barrett, 2017 ) against which sensory stimuli are initially matched to invoke emotional, cognitive, and behavioral responses ( Fazio et al., 1986 ; Bargh, 1989 ; Bargh et al., 1992 ). For instance, studies have shown that information is sent to pre-conscious regions of the brain (associated with emotion and the limbic system) 0.3 s before reaching higher conscious regions of the brain (associated with cognition; Libet, 1985 ; LeDoux, 1996 ). This implies that conscious thinking and the degree of choice a person has over their thoughts, decisions, and actions may be pre-influenced by earlier emotional responses, activated automatically by their implicit attitudes ( Bargh, 1989 ; Bargh and Morsella, 2008 ). Given that attitudes are intrinsically linked with emotional processing and emotion-driven behavior, and that all three elements (attitude, ability-EI, and behavioral-EE) operate both automatically and consciously, it may be beneficial to incorporate attitudes within a dual-processing framework of EI and EE.

Building upon existing integrated models, this may take the form of two parallel and interconnected streams, conscious and automated, as shown in Figure 1 . Each stream would comprise three components: attitudes, ability-EI, and behavioral-EE, with attitudes as antecedent to both ability-EI and behavioral-EE, as indicated by the horizontal lines. In the conscious stream, explicit attitudes (EA) may motivate an individual toward behavioral action and effective management of their emotions (EE b ). To perform the action they may also draw upon crystallized EI knowledge (EI c ). For example, an individual may consciously adopt a positive intention (their explicit attitude – EA) to be calm and relaxed, and then embed this attitude by rehearsing an affirmation to themselves, such as “I feel calm, composed, and relaxed” (EE b ). To further aid relaxation, they may learn a specific breathing technique (EI c ) which they implement daily (EE b ).

An attitude-based dual-processing framework of EI.

Through conscious practice, skill acquisition, and habit formation ( Fitts and Posner, 1967 ; Anderson, 1982 ; Sun et al., 1996 ), the three components may become proceduralised and automated such that explicit attitudes (EA) gradually become implicit (IA; Gawronski and Bodenhausen, 2006 ), emotion knowledge (EI c ) becomes a fluid automated process (EI a ), and conscious deliberate behaviors (EE b ) become skillful habits (EE h ). The process may also be reciprocal such that automatic and unconscious processes become conscious, such as becoming aware of unhelpful behavioral habits (EE h ) following 360-degree feedback or recognizing unconscious biases (IA) through awareness training. The bi-directional link between the conscious and automated streams is illustrated by the vertical lines in Figure 1 . Equally, some elements may be automated and others conscious, as indicated by the diagonal lines, such as an individual who has an unconscious fear of failure (IA) but is acutely aware of feeling anxious while completing tasks (EI c ) and consequently demonstrates perfectionist tendencies (EE h ).

As to which stream an individual takes, conscious or automated, may depend on whether the matter is sufficiently familiar and routine to be automated, or challenging enough that it meets the threshold for conscious awareness ( Baumeister et al., 2007 ). Drawing upon the neuroscientific literature, Baumeister et al. (2007 , p. 170) contend that “it is mainly the automatic affective responses that directly contributes to causing behavior.” Although the prevailing direction of influence in this model is from left to right (attitudes as antecedent to EI and EE), it is also recognized that there is a reciprocal influence in the opposite direction such that attitudes may shift to be congruent with cognition and behavior ( Festinger, 1957 ; Bem, 1965 , 1972 ). This reciprocal interaction, combined with the interdependence of the conscious and automated streams creates many interconnections between the three components that may form the basis of initial hypotheses for further investigation.

The inclusion of implicit and explicit attitudes as key determinants of EI and EE builds on several integrated and dual-processing models mentioned previously ( Ybarra et al., 2014 ; Joseph et al., 2015 ; Vesely-Maillefer et al., 2018 ). It provides an organizing framework for the relationship between conscious and automated processes and differentiates between ability-EI, its inputs (attitude), and outputs (behavioral-EE). It also strengthens the case for differentiating between conscious and automated processing of EI and EE. Including attitudes as antecedents to EI and EE raises the question as to what attitudes may facilitate emotionally intelligent processes and behavior.

3.2. Foundations for intrapersonal and interpersonal EI/EE

As well as being implicit and explicit, another feature of attitudes that relates closely to EI/EE is the differentiation between self and others. Attitudes may broadly be directed internally, toward oneself and the self-concept, or externally toward a person, place, object, or event ( Ajzen, 2007 ). Similarly, it has become more common within models of EI/EE, to distinguish between the intrapersonal (self) and interpersonal (others) domains ( Fiori and Vesely-Maillefer, 2018 ) with growing evidence to suggest that individual differences may exist between both streams ( Mikolajczak et al., 2015 ; Troth et al., 2018 ). Ybarra et al. (2014) comment that there is no reason to assume that someone strong in one area of EI, such as being aware of their feelings, will be capable in another area of EI, such as being aware of the feelings of others. Brasseur et al. (2013) postulate that in some cases intrapersonal EI may carry more weight than interpersonal EI (e.g., for managing job stress) but the opposite may be true in other cases (e.g., for building relationships).

An overarching attitude that may underpin the self and other streams of EI/EE is the concept of unconditional positive regard (UPR; Rogers, 1957 ), which may be applied to oneself (self-regard) or to others (regard for others). Rogers (1959 , p. 206) defines Positive Regard as “including such attitudes as warmth, liking, respect, sympathy, and acceptance,” and Lietaer (2001 , pp. 92–93) defines Unconditionally as “valuing the deeper core of the person.” For example, caring for someone even when disapproving of their actions, such as a parent’s love for their child, or maintaining feelings and self-worth even when underperforming at work. UPR was later applied within Transactional Analysis theory ( Berne, 1964 ) and defined as “one’s basic beliefs about self and others, which are used to justify decisions and behavior” ( Stewart and Jones, 1987 , p. 119). Self-regard is conceptually similar to the construct of self-esteem ( Blascovich and Tomaka, 1991 ), which has been intensively studied as a variable of personality psychology ( Costa et al., 1991 ; Judge and Bono, 2001 ; Zeigler-Hill et al., 2015 ). A classic definition of self-esteem is “it expresses an attitude of approval and indicates the extent to which an individual believes himself to be capable, significant, successful and worthy” ( Coopersmith, 1967 , pp. 4–5). Other-esteem (regard for others) is a newer concept first defined by Hwang (1995) and considered analogous to the definition of self-esteem (self-regard) as applied to others ( Bowles et al., 2013 ; Busse and Flowers, 2017 ). Both attitudes are closely related to self-concept theory, described as “an active structure that organizes and gives meaning to past and current experiences, provides goals and standards for behavior, and motivates future choices and actions” ( Harter, 2012 , cited in Keefer, 2015 , p. 10).

In a review of self-report assessments for emotional competencies, Keefer (2015 , p. 9) comments that “self-concept theory is conspicuously missing from the bulk of empirical research. I see this as a major oversight ….” This assertion is supported by applied research on self-efficacy ( Bandura, 1997 ), growth mindset ( Dweck and Leggett, 1988 ), core self-evaluation ( Judge and Bono, 2001 ), and other self-concept theories discussed later in this paper, that self-belief can greatly influence how successfully an individual may harness their potential to achieve effective outcomes and performance. Despite this view, there has been widespread criticism of measures that include self-concept scales such as self-regard as “irrelevant variables” that are “unmooring the concept” of EI ( Mayer et al., 2008 , pp. 504 and 508). Far from being irrelevant, it is argued that the attitudes of self-regard and regard for others can provide strong foundations for the intrapersonal and interpersonal streams of EI/EE that may positively influence behavior. Other established instruments such as the Bar-On EQi (1997) , the EQi 2.0 ( Multi-Health Systems, 2011 ), and the TEIQue ( Petrides et al., 2007 ) include the concept of self-esteem (self-regard), but do not include regard for others. This may be considered a significant omission, as the degree to which we value and accept others may greatly influence our social intentions, interaction, and behavior toward others. On this basis, it is proposed that the UPR attitudes of self-regard and regard for others may form underpinning attitudes to the intrapersonal and interpersonal streams of EI/EE.

Both attitudes may be incorporated within the dual-processing model shown in Figure 1 as either implicit or explicit attitudes ( Greenwald et al., 2002 ). An individual with higher implicit regard for others (IA) may be less judgmental of others, allowing them to better perceive and understand the emotions of others (EI a ), and in turn, be more open and empathic in their behavior (EE h ). Or an individual with lower implicit self-regard (IA), may repress uncomfortable feelings (EI a ) and become more rigid and defensive in their behavior (EE h ). There may also be a reciprocal influence where changes in behavior influence UPR attitudes. For example, prosocial behavior (EE) has been shown to evoke positive emotions (EI c ; Aknin et al., 2012 ) and enhance well-being (EA; Tabassum et al., 2016 ). Furthermore, there may be interaction between the self and other dimensions; for instance, it may be easier to empathize with the feelings of others (Interpersonal) if one has experienced and become aware of similar feelings in oneself (Intrapersonal; Gallup and Platek, 2002 ). Appling intrapersonal and interpersonal UPR attitudes to the dual-processing model may increase its complexity, but potentially provides a more complete and accurate representation of EI/EE in practice.

3.3. An ethical basis for EI/EE

Another area where the UPR attitudes may be of relevance is in the ethical application of EI/EE. Ethical considerations have been of ongoing concern within the EI literature, particularly for employment, leadership, and organizational settings ( Schlegelmilch and Thomas, 2011 ). Well before the popularization of EI, Goffman (1969) described the strategic manipulation and control of emotions to achieve personal gain, and Caruso and Salovey (2004 , p. 171) observe too that “A manager who is expert in managing emotions can use the ability to manipulate employees.” Kilduff et al. (2010) describe how the strategic disguise of one’s own emotions and the manipulation of others’ emotions may be used for self-serving purposes such as career ambition, or as they put it “getting ahead involves leaving others behind” (p. 146). Carr (2000 , p.31) argues that the value of EI “is dependent on the moral end which it serves” and Segon and Booth (2015) urge an ethical basis for EI, highlighting competency EI models as lacking ethical foundations. After careful examination of the Emotional Competency Inventory (ECI) and ESCI ( Sala, 2002 ; Boyatzis, 2007 ) scales and framework, they conclude that any ethical outcome is “a matter of moral luck” and certainly not part of the ECI competencies themselves. This, they suggest, leaves managers and leaders open to potential decisions and actions that are unethical, citing several cases of how the “corporate psychopath” ( Babiak and Hare, 2006 ) may display emotional competencies yet engage in corrupt and unethical practices. They further propose that attitudes, a key component of competencies, should be included within EI models, as they “enable the knowledge and behavior (competencies) to be applied in a way that demonstrates genuineness and authenticity” (p. 790). In current models of EI and EE, there is little reason to assume that higher emotional intelligence will necessarily produce more moral and ethical behavior, just as having higher cognitive intelligence does not confer greater moral values upon an individual.

Attitudes correspond closely with the expression of individuals’ values ( Ajzen, 2007 ), and moral values reflect a person’s ethical orientation. The inclusion of attitude as a basis for EI and EE presents an opportunity to instill ethical foundations in such models. One approach to EI/EE that may facilitate ethical behavior is the nine-layered pyramid of EI ( Drigas and Papoutsi, 2018 , 2021 ) in which the higher stages of EI development include self-actualization and transcendence, both of which relate closely to the UPR attitudes. According to the authors, “Self-actualizers feel empathy and kinship toward humanity” and “transcendence is strongly correlated with self-esteem, emotional well-being and global empathy” (p. 8). Self-transcendence also forms part of the Schwartz (2012) theory of basic values and is described as “enhancement of others and transcendence of selfish interests” (p. 9). A potential limitation of the pyramid model is that the higher levels of EI development are dependent on successful transition through the earlier stages, such as emotional recognition, perception (ability-EI), empathy, and social skills (behavioral-EE). These may not be easily achieved if an individual already holds hedonistic or self-serving attitudes.

Another approach to EI/EE that has ethical foundations is the Emotional Intelligence Profile (EIP; Maddocks, 2018 ). It includes a set of eight foundational attitudes or guiding principles that may facilitate more emotionally intelligent and ethical behaviors. The primary principle, “however you and others are, is okay,” reflects Unconditional Positive Regard and is represented by the EIP scale facets of Self Regard and Regard for Others. A second principle, “People have a natural tendency toward growth,” is drawn from the term Physis (Aristotle, Physics, Book II, Chapter 1), that all living things, including people, are endowed with innate resources to thrive and grow. Adopting this philosophical assumption may encourage greater belief in others, and a more positive, supportive, and encouraging interpersonal environment. Another EIP principle, “people are different,” is taken from the phenomenological position that people experience the world differently, which may encourage greater awareness, understanding, and appreciation of others and their individual differences.

The main thrust of this position is that having higher regard for others will promote more ethical EI/EE. Competencies such as awareness of others and empathy may enable an individual to “read and understand the motivations of others” ( Bar-On, 2000 ), but may be utilized for entirely different purposes depending on whether the individual’s intentional attitude is self-serving (low regard for others) or altruistic (high regard for others). It would seem incompatible for unethical EE behaviors to be present in an individual who has unconditional regard for others. Prosocial attitudes are capable of guiding prosocial actions across different domains (e.g., Balconi and Canavesio, 2013 ). For example, studies of empathy, prosocial, and moral identity (i.e., a person who believes that helping others is at the core of who they are), have found these attitudes to strengthen readiness to engage in prosocial and ethical behavior ( Hardy and Carlo, 2011 ; Davis, 2015 ; Hertz and Krettenauer, 2016 ). Self-regard too may impact moral behavior. Longitudinal research over 30 years (summarized in Kaplan, 1995 ) shows direct and indirect effects of low self-esteem (also described as negative self-attitudes and self-derogation) on deviant behavior. The proposition that UPR attitudes support ethical EI practices is consistent with the position held by Segon and Booth (2015 , p. 790) that “ethical management” should be central to the measurement of EI and that ethical attitudes help demonstrate ethical behaviors.

3.4. Balance between the extremes of EI/EE

Another related concern that may be addressed by the UPR attitudes of self-regard and regard for others is non-linearity or the “dark side” of EI/EE constructs. EI is generally considered to fall under the umbrella of positive psychology ( Salovey et al., 2002 ) and there is indeed substantial evidence for its positive impact ( Furnham and Petrides, 2003 ; Austin et al., 2005 ; Day et al., 2005 ). However, Kilduff et al. (2010 p. 147) express concern at the “overly-positive celebration of EI” and the imbalance of research focusing almost exclusively on the prosocial aspects of EI ( Antonakis and Dietz, 2010 ). Dasborough et al. (2021) call on scholars for greater balance, to examine negative as well as positive outcomes of EI in future research endeavors. Following their meta-analysis on EI and the Dark Triad, Miao et al. (2019) recommend that “future studies may investigate whether there is a ‘too much of a good thing’ effect of EI” and “explore the possibilities of curvilinear relationships” (pp. 195–6). This concurs with an earlier review of the literature, which “points to the possibility of ‘optimal’ levels of EI – both within and across constructs” ( Davis and Nichols, 2016 , p. 1). Several commentators have argued the benefits of exploring non-linear aspects of personality and behavior ( Benson and Campbell, 2007 ; Le et al., 2011 ), which remains a contentious issue ( Walmsley et al., 2018 ), and has rarely been applied to the measurement of EI or EE ( Maddocks et al., 2020 ).

The potential for non-linear patterns of EI/EE, which recognize both optimal and sub-optimal polarities, may in part be represented by the UPR attitudes of self-regard and regard for others. Overuse, or ‘too much’ of an EI/EE facet may be driven by a combination of high self-regard and low regard for others, suggesting that an individual has a degree of arrogance (i.e., I am more valuable than others). 2 Examples of this may include being too assertive (aggressive), too independent, mistrusting, emotionally over-controlled, and overly optimistic. The bipolar opposite to this, ‘too little’ EI/EE, may be driven by a combination of low self-regard and high regard for others (i.e., others are more valuable than me) and be associated with self-deprecating tendencies such as passive, dependent, over-trusting, emotionally under-controlled, and pessimistic. The optimal or balanced position that represents higher EI/EE would be reflected in the combination of high self-regard and high regard for others (i.e., holding the UPR attitude of value and acceptance toward oneself and others). Examples of this may include being assertive, emotionally balanced, cautiously trusting, and realistically optimistic. Attaining an optimal level of EI/EE is consistent with Aristotle’s human virtue of finding “the mean between the extremes” ( De George, 1999 ) in which there is a “golden mean” that provides a form of self-control and moderation between the extremes and excesses that may cause harm and disadvantage to individuals and others in society. Achieving a virtuous balance between extremes also aligns with the premise that UPR attitudes support an ethical basis for EI/EE.

By representing both the optimal and sub-optimal polarities of EI/EE, the UPR attitudes may also reflect some of the behavioral variation inherent in emotion-related facets of EI and EE. Several scholars express concern that the use of psychometric questionnaires to measure subjective emotions and EI is too restrictive ( Ashton-James, 2003 ; Lindebaum, 2009 ) and argue that “boxing” emotions by numbers bears crude resemblance to the complexities of a person’s affective life ( Fineman, 2004 ). This concern may partly be considered through the lens of UPR attitudes. For example, a common facet of behavioral-EE is conflict handling, which in theory will manifest as either optimal (assertive) or sub-optimal (passive or aggressive) behavior. However, in practice, there are often more subtle, dynamic, and variable interactions between the three elements of conflict handling. For instance, an individual who is passive may also become aggressive, due in part to “surface acting” i.e., the emotional labor of withholding feelings ( Hochschild, 1983 ). This rebound from one extreme to the other may also occur in several other facets of EI/EE such as trust – an individual who is over-trusting is more likely to be let down by others causing them to become mistrusting, and optimism – an individual who is overoptimistic is more likely to experience failure which might cause them to feel despondent and pessimistic. Given that the UPR attitudes can capture the optimal and sub-optimal elements of EI/EE, they may be incorporated within models and measures of EI/EE to reflect the dynamic variability in these facets.

As discussed, the UPR attitudes of self-regard and regard provide a coherent foundation to the intrapersonal and interpersonal pillars of EI/EE that offers several constructive benefits to understanding and applying EI/EE. It is not suggested that all aspects of EI/EE be drawn back to the UPR attitudes, however, given the emphasis on EI/EE being an adaptive quality, it would seem incumbent on theorists in this field to represent the ethical, optimal, and sub-optimal elements of EI/EE within a coherent and integrated model.

3.5. Supporting the development of EI/EE

A further benefit that an attitude-based approach to EI and EE offers is that attitudes are malleable and can be developed ( Petty and Cacioppo, 1986 ; Chaiken, 1987 ), more so than may be attributable to the intelligence components of EI ( Mayer and Cobb, 2000 ) or the more “static nature” of trait EE facets ( Côté, 2014 ; Alessandri et al., 2015 , p. 27). A trigger for the early popularization of EI was the publication, “Emotional Intelligence; Why it can matter more than IQ” ( Goleman, 1995 ), which captured the interest of the business world. This was seen as an egalitarian rebuttal to “The Bell Curve” of Herrnstein and Murray (1994) , which argued the importance of IQ for understanding social class in society. IQ was seen by many as hard, elitist, and difficult to develop, while EI (or EQ) was seen to be kind, and something that all people could develop. Although many of the grander claims by Goleman have since been moderated ( Emmerling and Goleman, 2003 ), there is growing evidence that aspects of EI/EE can be developed. Two meta-analytic studies have demonstrated that EI and EE can be improved through training interventions ( Hodzic et al., 2017 ; Mattingly and Kraiger, 2019 ). The authors of the second study conclude; “The moderate and positive effect of training on EI supports the malleability of this construct, allowing us to infer that EI is trainable.” (p. 152).

Despite emerging evidence for the trainability of EI and EE, it would be difficult to attribute this to changes in either aspects of intelligence or personality traits. Intelligence is broadly regarded as a stable attribute over time ( Kagan, 1980 ), as are dispositional personality traits ( Costa and McCrae, 1997 ). However, there may be elements within both models of EI and EE that are more open to development, in particular elements of self-concept, attitude, and self-belief ( Bandura, 1997 ; Marsh and Craven, 2006 ; Forgas et al., 2010 ), that may reinforce self-perceived emotional competence ( Keefer, 2015 ). A primary example is beliefs in emotional self-efficacy, which have been found to help individuals express positive emotions, regulate negative emotions, promote prosocial behavior, and support their self-esteem ( Alessandri et al., 2015 ). Similarly, core self-evaluation (CSE), which represents the general and fundamental beliefs individuals hold about themselves, such as their self-esteem and self-efficacy ( Judge and Bono, 2001 ), has been shown to have strong links to trait EI (e.g., r = 0.78, Kluemper, 2008 ) and be predictive of positive work outcomes such as job satisfaction, job performance, and well-being ( Caprara and Steca, 2005 ).

A related avenue of self-development for EI and EE are implicit theories ( Dweck and Leggett, 1988 ) which hold that people who have a growth mindset (incremental theories) and believe that emotions, intelligence, and behavior can be changed, are more likely to put in the hard work and strategies to make this happen ( Aronson et al., 2002 ; Blackwell et al., 2007 ). Implicit theories of emotions have shown that those holding incremental theories more frequently use cognitive reappraisal as an emotional regulation strategy, experience more positive and fewer negative emotions, receive greater social support, are more likely to use mastery-oriented strategies rather than helpless strategies, and harbor higher expectations of success ( Tamir et al., 2007 ; Burnette et al., 2013 ; De Castella et al., 2013 ). A growth mindset has also been associated with higher EI. Perreault et al. (2014) found that general self-determination (GSD) could account for individual variations in EI, and other studies suggest that people’s implicit theories about EI may influence their emotional abilities ( Cabello and Fernández-Berrocal, 2015 ).

Another self-development methodology linked to attitude and EI is mindfulness, described as an attitude characterized by nonjudgment of, and openness to, current experience ( Bishop et al., 2004 ; Brown and Ryan, 2004 ). A systematic review of an eight-week mindfulness-based stress reduction program (MBSR) found that MBSR led to changes in the brain’s amygdala consistent with improved emotional regulation ( Gotink et al., 2016 ). A meta-analytic review ( Miao et al., 2018 ) found EI had a statistically significant association with trait mindfulness which increased with age, suggesting that mindfulness practice encourages the development of key abilities and competencies associated with EI. As an approach to self-development, mindfulness resonates well with the view of Lindebaum (2009) that changing one’s attitude to focus on the present is synonymous with EI and in turn, leads to behavioral change. These examples lend credence to the view that attitude plays an important role in the development of EI and EE.

Further endorsement for attitudes supporting EI/EE development can be drawn from the benefits previously discussed. The case was made for there being a close relationship between attitude, ability-EI, and behavioral-EE, and that changes in attitude may facilitate a corresponding change in EI and EE. Moreover, changes in EI/EE may be more sustainable and enduring when congruent with a person’s attitudes. This is supported by dual processing theories of EI/EE which, as proposed, may be extended to include conscious (explicit) and automatic (implicit) attitudes. A key feature of dual-processing, essential to personal development, is that conscious procedural practices may become skilled, habitual, and enduring, and that explicit attitudes may become implicit and automated through repetition and experience over time ( Gawronski and Bodenhausen, 2006 ). In which case, greater emphasis could be placed on building automaticity to facilitate longer-term, sustainable change in behavior, much sought after by organizations investing in employee development. A limitation of implicit attitudes is that they are unconscious and not open to self-report, which may render them difficult to change. However, being unconscious and automated does not mean that implicit attitudes and processes are inaccessible and cannot be brought into conscious awareness or developed. Through introspection and self-observation, a person may become aware of their feeling, thinking, and behavioral patterns, giving clues as to their unconscious attitudes ( Bem, 1972 , p. 2) which may in itself cause them to change their attitudes ( Wilson et al., 1989 ). Self-awareness training may also help an individual become aware of other unconscious ‘blind spots’ such as automatic emotional triggers, defensive habits, and unconscious biases, that may further support personal development. When considering what attitudes facilitate EI/EE, the case was made for a humanistic approach that encourages ethical behavior. Humanistic attitudes are inherently developmental, as they start from the assumption that people are predisposed toward growth ( Maslow, 1962 ; Alicke and Sedikides, 2009 ). This was identified in the UPR attitudes of self-regard and regard for others, the combination of which help differentiate between optimal and sub-optimal aspects of EI/EE. Greater understanding and awareness of the antecedents to maladaptive behavior may enable an individual to address the causes of lower EI/EE and facilitate their personal development.

Given that attitudes play an important role in driving emotions, fueling cognition, and influencing behavior, they are noticeably absent from models of EI and EE. This may be seen as a significant gap for a concept that has considerable potential for enhancing self-awareness and personal development. As proposed, attitudes may be incorporated as foundational elements to EI/EE that promote personal and interpersonal improvement.

4. Conclusion

In this paper, it has been proposed that attitudes provide an important foundation for integrated models of ability-EI and EE traits and competencies. These models vary in their proposition, some present EI and EE as parallel processes, and others as sequential stages. One reason for this may be a lack of differentiation between the ontological status of EE facets, some of which may be antecedent to EI (such as attitudes, beliefs, and values), and others’ outcomes of EI (such as behavioral skills and habits). Given the close relationship between attitude, emotion, cognition, and behavior, it is argued that attitudes play an important role as potential precursors of EI and EE which should be incorporated within integrated models and explored through further research.

Several benefits may be derived from the inclusion of attitudes as a basis for EI/EE that help address some current concerns with existing approaches. The first of these is a dual-processing model for EI/EE, an aspect often overlooked by researchers in this field. It was proposed that explicit attitudes may underpin the conscious processing of EI/EE, and implicit attitudes may underpin the automated processing of EI/EE. The combination of these three components (attitude, EI, and EE) and their two streams (conscious and automated) builds upon existing integrated models of EI/EE. The relationship between attitude, EI, and EE has still to be tested empirically, for which this paper may provide a stimulus for initial hypotheses.

Another concern that may be addressed by underlying attitudes, is providing an ethical basis for EI/EE. Drawn from humanistic psychology and the principle of Unconditional Positive Regard (UPR), two core attitudes were recommended, self-regard and regard for others. These attitudes may also underpin the two core pillars of EI/EE, intrapersonal (self) and interpersonal (others), a distinction often missing from other integrated approaches to EI/EE. The combination of both attitudes may address other concerns within the EI/EE literature, namely, they may differentiate the optimal and sub-optimal elements of EI/EE, provide balance between the extremes of too much and too little EI/EE, and represent dynamic variability inherent in some facets of EI/EE.

A broader implication and further benefit of an attitude-based approach to EI/EE is that it may better support the application of EI and EE for personal development. Specifically: attitudes may be more malleable than the intelligence and trait components of EI/EE; individual changes in EI and EE may be more sustainable when they are congruent with underlying attitudes; attitudes may help to understand and address the antecedents of maladaptive EI/EE; and changes in EI/EE are more likely to become automated and habitual if they are embedded as implicit attitudes. Providing attitude-based coaching and development in EI/EE could be a valuable avenue for further exploration and experimental research.

Data availability statement

Author contributions.

The author confirms being the sole contributor of this work and has approved it for publication.

This study was funded by Talogy. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

Conflict of interest

JM was employed by company Talogy.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1 Attitudes are closely intertwined with other social-cognitive constructs that sit within the lower-order levels of personality space ( McCrae and Costa, 2008 ), such as beliefs and values. According to Schwartz (2012) a crucial difference is in how they vary: attitudes are evaluative and vary on a positive/negative scale, beliefs vary in how certain we are that something is true, and values vary in importance as guiding principles in life. All three share a common theme of influencing decisions and motivating behavior.

2 Although these behaviors present as having high or excessive explicit self-regard they are also potentially masking low implicit self-regard ( Bosson et al., 2008 ), i.e., being critical of others may be an unconscious coping strategy to avoid being critical of oneself. This also helps explain the apparent contradiction of having "too much of a good thing."

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Chapter 4. Attitudes, Behavior, and Persuasion

4.1 Exploring Attitudes

Learning Objectives

• Define the concept of an attitude and explain why it is of such interest to social psychologists.
• Review the variables that determine attitude strength.
• Outline the factors that affect the strength of the attitude-behavior relationship.

Although we might use the term in a different way in our everyday life (e.g., “Hey, he’s really got an attitude !”), social psychologists reserve the term attitude to refer to our relatively enduring evaluation of something, where the something is called the attitude object . The attitude object might be a person, a product, or a social group (Albarracín, Johnson, & Zanna, 2005; Wood, 2000). In this section, we will consider the nature and strength of attitudes and the conditions under which attitudes best predict our behaviors.

Attitudes Are Evaluations

When we say that attitudes are evaluations, we mean that they involve a preference for or against the attitude object, as commonly expressed in terms such as prefer , like , dislike , hate , and love . When we express our attitudes—for instance, when we say, “I like swimming,” “I hate snakes,” or “I love my parents” —we are expressing the relationship (either positive or negative) between the self and an attitude object. Statements such as these make it clear that attitudes are an important part of the self-concept.

Every human being holds thousands of attitudes, including those about family and friends, political figures, abortion rights, terrorism, preferences for music, and much more. Each of our attitudes has its own unique characteristics, and no two attitudes come to us or influence us in quite the same way. Research has found that some of our attitudes are inherited, at least in part, via genetic transmission from our parents (Olson, Vernon, Harris, & Jang, 2001). Other attitudes are learned mostly through direct and indirect experiences with the attitude objects (De Houwer, Thomas, & Baeyens, 2001). We may like to ride roller coasters in part because our genetic code has given us a thrill-loving personality and in part because we’ve had some really great times on roller coasters in the past. Still other attitudes are learned via the media (Hargreaves & Tiggemann, 2003; Levina, Waldo, & Fitzgerald, 2000) or through our interactions with friends (Poteat, 2007). Some of our attitudes are shared by others (most of us like sugar, fear snakes, and are disgusted by cockroaches), whereas other attitudes—such as our preferences for different styles of music or art—are more individualized.

Table 4.1, “Heritability of Some Attitudes,” shows some of the attitudes that have been found to be the most highly heritable (i.e., most strongly determined by genetic variation among people). These attitudes form earlier and are stronger and more resistant to change than others (Bourgeois, 2002), although it is not yet known why some attitudes are more genetically determined than are others.

Our attitudes are made up of cognitive, affective, and behavioral components. Consider an environmentalist’s attitude toward recycling, which is probably very positive:

• In terms of affect: They feel happy when they recycle.
• In terms of behavior: They  regularly recycle their bottles and cans.
• In terms of cognition: They believe recycling is the responsible thing to do.

Although most attitudes are determined by affect, behavior, and cognition, there is nevertheless variability in this regard across people and across attitudes. Some attitudes are more likely to be based on feelings, some are more likely to be based on behaviors, and some are more likely to be based on beliefs. For example, your attitude toward chocolate ice cream is probably determined in large part by affect—although you can describe its taste, mostly you may just like it. Your attitude toward your toothbrush, on the other hand, is probably more cognitive (you understand the importance of its function). Still other of your attitudes may be based more on behavior. For example, your attitude toward note-taking during lectures probably depends, at least in part, on whether or not you regularly take notes.

Different people may hold attitudes toward the same attitude object for different reasons. For example, some people vote for politicians because they like their policies, whereas others vote for (or against) politicians because they just like (or dislike) their public persona. Although you might think that cognition would be more important in this regard, political scientists have shown that many voting decisions are made primarily on the basis of affect. Indeed, it is fair to say that the affective component of attitudes is generally the strongest and most important (Abelson, Kinder, Peters, & Fiske, 1981; Stangor, Sullivan, & Ford, 1991).

Human beings hold attitudes because they are useful. Particularly, our attitudes enable us to determine, often very quickly and effortlessly, which behaviors to engage in, which people to approach or avoid, and even which products to buy (Duckworth, Bargh, Garcia, & Chaiken, 2002; Maio & Olson, 2000). You can imagine that making quick decisions about what to avoid or approach has had substantial value in our evolutionary experience. For example:

• Snake = bad ⟶ run away
• Blueberries = good ⟶ eat

Because attitudes are evaluations, they can be assessed using any of the normal measuring techniques used by social psychologists (Banaji & Heiphetz, 2010). Attitudes are frequently assessed using self-report measures, but they can also be assessed more indirectly using measures of arousal and facial expressions (Mendes, 2008) as well as implicit measures of cognition, such as the Implicit Association Test (IAT). Attitudes can also be seen in the brain by using neuroimaging techniques. This research has found that our attitudes, like most of our social knowledge, are stored primarily in the prefrontal cortex but that the amygdala is important in emotional attitudes, particularly those associated with fear (Cunningham, Raye, & Johnson, 2004; Cunningham & Zelazo, 2007; van den Bos, McClure, Harris, Fiske, & Cohen, 2007). Attitudes can be activated extremely quickly—often within one-fifth of a second after we see an attitude object (Handy, Smilek, Geiger, Liu, & Schooler, 2010).

Some Attitudes Are Stronger Than Others

Some attitudes are more important than others because they are more useful to us and thus have more impact on our daily lives. The importance of an attitude, as assessed by how quickly it comes to mind , is known as attitude strength (Fazio, 1990; Fazio, 1995; Krosnick & Petty, 1995). Some of our attitudes are strong attitudes, in the sense that we find them important, hold them with confidence, do not change them very much, and use them frequently to guide our actions. These strong attitudes may guide our actions completely out of our awareness (Ferguson, Bargh, & Nayak, 2005).

Other attitudes are weaker and have little influence on our actions. For instance, John Bargh and his colleagues (Bargh, Chaiken, Raymond, & Hymes, 1996) found that people could express attitudes toward nonsense words such as juvalamu (which people liked) and chakaka (which they did not like). The researchers also found that these attitudes were very weak.

Strong attitudes are more cognitively accessible—they come to mind quickly, regularly, and easily. We can easily measure attitude strength by assessing how quickly our attitudes are activated when we are exposed to the attitude object. If we can state our attitude quickly, without much thought, then it is a strong one. If we are unsure about our attitude and need to think about it for a while before stating our opinion, the attitude is weak.

Attitudes become stronger when we have direct positive or negative experiences with the attitude object, and particularly if those experiences have been in strong positive or negative contexts. Russell Fazio and his colleagues (Fazio, Powell, & Herr, 1983) had people either work on some puzzles or watch other people work on the same puzzles. Although the people who watched ended up either liking or disliking the puzzles as much as the people who actually worked on them, Fazio found that attitudes, as assessed by reaction time measures, were stronger (in the sense of being expressed quickly) for the people who had directly experienced the puzzles.

Because attitude strength is determined by cognitive accessibility, it is possible to make attitudes stronger by increasing the accessibility of the attitude. This can be done directly by having people think about, express, or discuss their attitudes with others. After people think about their attitudes, talk about them, or just say them out loud, the attitudes they have expressed become stronger (Downing, Judd, & Brauer, 1992; Tesser, Martin, & Mendolia, 1995). Because attitudes are linked to the self-concept, they also become stronger when they are activated along with the self-concept. When we are looking into a mirror or sitting in front of a TV camera, our attitudes are activated and we are then more likely to act on them (Beaman, Klentz, Diener, & Svanum, 1979).

Attitudes are also stronger when the ABCs of affect, behavior, and cognition all align. As an example, many people’s attitude toward their own nation is universally positive. They have strong positive feelings about their country, many positive thoughts about it, and tend to engage in behaviors that support it. Other attitudes are less strong because the affective, cognitive, and behavioral components are each somewhat different (Thompson, Zanna, & Griffin, 1995). Your cognitions toward physical exercise may be positive—you believe that regular physical activity is good for your health. On the other hand, your affect may be negative—you may resist exercising because you prefer to engage in tasks that provide more immediate rewards. Consequently, you may not exercise as often as you believe you ought to. These inconsistencies among the components of your attitude make it less strong than it would be if all the components lined up together.

When Do Our Attitudes Guide Our Behavior?

Social psychologists (as well as advertisers, marketers, and politicians) are particularly interested in the behavioral aspect of attitudes. Because it is normal that the ABCs of our attitudes are at least somewhat consistent, our behavior tends to follow from our affect and cognition. If I determine that you have more positive cognitions about and more positive affect toward waffles than French toast, then I will naturally predict (and probably be correct when I do so) that you’ll be more likely to order waffles than French toast when you eat breakfast at a restaurant. Furthermore, if I can do something to make your thoughts or feelings toward French toast more positive, then your likelihood of ordering it for breakfast will also increase.

The principle of attitude consistency (that for any given attitude object, the ABCs of affect, behavior, and cognition are normally in line with each other ) thus predicts that our attitudes (for instance, as measured via a self-report measure) are likely to guide behavior . Supporting this idea, meta-analyses have found that there is a significant and substantial positive correlation among the different components of attitudes, and that attitudes expressed on self-report measures do predict behavior (Glasman & Albarracín, 2006).

However, our attitudes are not the only factor that influence our decision to act. The theory of planned behavior , developed by Martin Fishbein and Izek Ajzen (Ajzen, 1991; Fishbein & Ajzen, 1975), outlines three key variables that affect the attitude-behavior relationship: (a) the attitude toward the behaviour (the stronger the better), (b) subjective norms (the support of those we value), and (c) perceived behavioral control (the extent to which we believe we can actually perform the behavior). These three factors jointly predict our intention to perform the behavior, which in turn predicts our actual behavior (Figure 4.2, “Theory of Planned Behavior”).

To illustrate, imagine for a moment that your friend Sharina is trying to decide whether to recycle her used laptop batteries or just throw them away. We know that her attitude toward recycling is positive—she thinks she should do it—but we also know that recycling takes work. It’s much easier to just throw the batteries away. But if Sharina feels strongly about the importance of recycling, if her family and friends are also in favor of recycling, and if she has easy access to a battery recycling facility, then she will develop a strong intention to perform the behavior and likely follow through on it.

Since it was first proposed, the theory of planned behavior has grown to become an extremely influential model for predicting human social behavior. However, although it has been used to study virtually every kind of planned behavior, a recent meta-analysis of 206 articles found that this model was especially effective at predicting physical activity and dietary behaviors (McEachan, Conner, Taylor, & Lawton, 2011).

More generally, research has also discovered that attitudes predict behaviors well only under certain conditions and for some people. These include:

• When the attitude and the behavior both occur in similar social situations
• When the same components of the attitude (either affect or cognition) are accessible when the attitude is assessed and when the behavior is performed
• When the attitudes are measured at a specific, rather than a general, level
• For low self-monitors (rather than for high self-monitors)

The extent of the match between the social situations in which the attitudes are expressed and the behaviors are engaged in is important; there is a greater attitude-behavior correlation when the social situations match. Imagine for a minute the case of Magritte, a 16-year-old high school student. Magritte tells her parents that she hates the idea of smoking cigarettes. Magritte’s negative attitude toward smoking seems to be a strong one because she’s thought a lot about it—she believes that cigarettes are dirty, expensive, and unhealthy. But how sure are you that Magritte’s attitude will predict her behavior? Would you be willing to bet that she’d never try smoking when she’s out with her friends?

You can see that the problem here is that Magritte’s attitude is being expressed in one social situation (when she is with her parents), whereas the behavior (trying a cigarette) is going to occur in a very different social situation (when she is out with her friends). The relevant social norms are of course much different in the two situations. Magritte’s friends might be able to convince her to try smoking, despite her initial negative attitude, when they entice her with peer pressure. Behaviors are more likely to be consistent with attitudes when the social situation in which the behavior occurs is similar to the situation in which the attitude is expressed (Ajzen, 1991; LaPiere, 1936).

Research Focus

Attitude-Behavior Consistency

Another variable that has an important influence on attitude-behavior consistency is the current cognitive accessibility of the underlying affective and cognitive components of the attitude. For example, if we assess the attitude in a situation in which people are thinking primarily about the attitude object in cognitive terms, and yet the behavior is performed in a situation in which the affective components of the attitude are more accessible, then the attitude-behavior relationship will be weak. Wilson and Schooler (1991) showed a similar type of effect by first choosing attitudes that they expected would be primarily determined by affect—attitudes toward five different types of strawberry jam. They asked a sample of college students to taste each of the jams. While they were tasting, one-half of the participants were instructed to think about the cognitive aspects of their attitudes to these jams—that is, to focus on the reasons they held their attitudes—whereas the other half of the participants were not given these instructions. Then all the students completed measures of their attitudes toward each of the jams.

Wilson and his colleagues then assessed the extent to which the attitudes expressed by the students correlated with taste ratings of the five jams as indicated by experts at Consumer Reports . They found that the attitudes expressed by the students correlated significantly higher with the expert ratings for the participants who had not listed their cognitions first. Wilson and his colleagues argued that this occurred because our liking of jams is primarily affectively determined—we either like them or we don’t. And the students who simply rated the jams used their feelings to make their judgments. On the other hand, the students who were asked to list their thoughts about the jams had some extra information to use in making their judgments, but it was information that was not actually useful. Therefore, when these students used their thoughts about the jam to make the judgments, their judgments were less valid.

MacDonald, Zanna, and Fong (1996) showed male college students a video of two other college students, Mike and Rebecca, who were out on a date. According to random assignment to conditions, half of the men were shown the video while sober and the other half viewed the video after they had had several alcoholic drinks. In the video, Mike and Rebecca go to the campus bar and drink and dance. They then go to Rebecca’s room, where they end up kissing passionately. Mike says that he doesn’t have any condoms, but Rebecca says that she is on the pill.

At this point the film clip ends, and the male participants are asked about their likely behaviors if they had been Mike. Although all men indicated that having unprotected sex in this situation was foolish and irresponsible, the men who had been drinking alcohol were more likely to indicate that they would engage in sexual intercourse with Rebecca even without a condom. One interpretation of this study is that sexual behavior is determined by both cognitive factors (e.g., “I know that it is important to practice safe sex and so I should use a condom”) and affective factors (e.g., “Sex is enjoyable, I don’t want to wait”). When the students were intoxicated at the time the behavior was to be performed, it seems likely the affective component of the attitude was a more important determinant of behavior than was the cognitive component.

One other type of match that has an important influence on the attitude-behavior relationship concerns how we measure the attitude and behavior. Attitudes predict behavior better when the attitude is measured at a level that is similar to the behavior to be predicted. Normally, the behavior is specific, so it is better to measure the attitude at a specific level too. For instance, if we measure cognitions at a very general level (e.g., “Do you think it is important to use condoms?”; “Are you a religious person?”) we will not be as successful at predicting actual behaviors as we will be if we ask the question more specifically, at the level of behavior we are interested in predicting (e.g., “Do you think you will use a condom the next time you have sex?”; “How frequently do you expect to attend church in the next month?”). In general, more specific questions are better predictors of specific behaviors, and thus if we wish to accurately predict behaviors, we should remember to attempt to measure specific attitudes. One example of this principle is shown in Figure 4.3, “Predicting Behavior from Specific and Nonspecific Attitude Measures.” Davidson and Jaccard (1979) found that they were much better able to predict whether women actually used birth control when they assessed the attitude at a more specific level.

Attitudes also predict behavior better for some people than for others. As we saw in Chapter 3, self-monitoring refers to individual differences in the tendency to attend to social cues and to adjust one’s behavior to one’s social environment. To return to our example of Magritte, you might wonder whether she is the type of person who is likely to be persuaded by peer pressure because she is particularly concerned with being liked by others. If she is, then she’s probably more likely to want to fit in with whatever her friends are doing, and she might try a cigarette if her friends offer her one. On the other hand, if Magritte is not particularly concerned about following the social norms of her friends, then she’ll more likely be able to resist the persuasion. High self-monitors are those who tend to attempt to blend into the social situation in order to be liked; low self-monitors are those who are less likely to do so. You can see that, because they allow the social situation to influence their behaviors, the relationship between attitudes and behavior will be weaker for high self-monitors than it is for low self-monitors (Kraus, 1995).

Key Takeaways

• The term attitude refers to our relatively enduring evaluation of an attitude object.
• Our attitudes are inherited and also learned through direct and indirect experiences with the attitude objects.
• Some attitudes are more likely to be based on beliefs, some are more likely to be based on feelings, and some are more likely to be based on behaviors.
• Strong attitudes are important in the sense that we hold them with confidence, we do not change them very much, and we use them frequently to guide our actions.
• Although there is a general consistency between attitudes and behavior, the relationship is stronger in some situations than in others, for some measurements than for others, and for some people than for others.

Exercises and Critical Thinking

• Describe an example of a behavior that you engaged in that might be explained by the theory of planned behavior. Include each of the components of the theory in your analysis.
• Consider a time when you acted on your own attitudes and a time when you did not act on your own attitudes. What factors do you think determined the difference?

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Image Descriptions

Figure 4.2 Theory of Planned Behavior

A flow chart explaining the theory of planned behavior. It starts with 3 types of beliefs that are connected by arrows:

• Behavioural beliefs – attitude towards the behaviour
• Normative beliefs – subjective norm
• Control beliefs – perceived behavioural control

The beliefs leads to intention, along with the actual behavioural control that is derived from the perceived behavioural control, then leads to behaviour.

[Return to Figure 4.2]

Figure 4.3 Predicting Behavior from Specific and Nonspecific Attitude Measures

A a bar graph with correlation on the y-axis, and attitude question on the x-axis. The attitude questions with the lowest to high correlation are: attitude toward birth control, attitude toward birth control pills, attitude toward using birth control pills, attitude toward using birth control pills during the next 2 years.

[Return to Figure 4.3]

Media Attributions

• “Theory of Planned Behavior” is adapted by Hilda Aggregani and licensed under a CC BY 4.0 licence .
• “Predicting Behavior from Specific and Nonspecific Attitude Measures” is based on data from Davidson and Jaccard (1979).Davidson, A. R., & Jaccard, J. J. (1979). Variables that moderate the attitude-behavior relation: Results of a longitudinal survey. Journal of Personality and Social Psychology, 37(8), 1364–1376.

Relatively enduring evaluation of something, where the something is called the attitude object.

The importance of an attitude, as assessed by how quickly it comes to mind.

For any given attitude object, the ABCs of affect, behavior, and cognition are normally in line with each other.

Principles of Social Psychology - 1st International H5P Edition Copyright © 2022 by Dr. Rajiv Jhangiani and Dr. Hammond Tarry is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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From attitudes to behaviour: Basic and applied research on the theory of planned behaviour

• Published: September 2003
• Volume 22 , pages 187–195, ( 2003 )

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• Julie Christian 2  

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The present article traces the development of the theory of planned behaviour, from early research on the attitude-behaviour relationship through the theory of reasoned action. In particular, it is argued that a perceived lack of correspondence between attitude and behaviour led to examination of variables that either moderated (e.g., attitude strength, measurement correspondence) or mediated (behavioural intention) the relationship between attitudes and behaviour. Several meta-analytic reviews provide strong empirical support for the theory of planned behaviour, yet several applied and basic issues need to be resolved. The six papers that make up the remainder of this special issue address several of these issues.

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When Do Attitudes Predict Behavior?

Why measure attitudes if people do not always do what they say they will.

Posted December 15, 2020 | Reviewed by Ekua Hagan

The failure of so many polls to predict the outcomes and margins of the last two presidential elections underscores the maddening challenge faced by social scientists who try to predict actual behavior from the expressed attitudes of the people that they sample.

Psychologists have been aware of this dilemma since at least the 1930s. In 1934, Stanford psychologist Richard LaPierre published a classic study in which the anti-Chinese prejudice expressed by 184 restaurants and 66 hotels across the United States resulted in only one refusal to accommodate a group of patrons that included several Chinese individuals. All of these establishments had indicated beforehand that they would not allow Chinese patrons on the premises.

Along these same lines, more recent studies have repeatedly demonstrated that attitudes toward things like attending church or donating blood are often not terribly useful when it comes to predicting who will and who will not engage in these activities.

So, should psychologists just give up trying?

The answer to this question is a clear “No.” Since the 1970s, new statistical tools fueled by the development of computers and more sophisticated research methodologies such as meta-analysis have convinced most psychologists that attitudes, at least some of the time, can be valuable predictors of behavior. The trick now is to understand under exactly what circumstances they are effective.

The information I am about to share is based upon the consensus of thousands of different studies over the past 40 years.

1. Attitudes Are Better Predictors When They Are Strong and Stable

Think for a moment about the thousands of attitudes that you hold. Some of these attitudes, perhaps about politics or religion (which is why we should avoid these topics in most social situations), are very important to you and may even be an essential part of your self-concept . Other attitudes, about different flavors of ice cream, for example, are much more tangential and less strongly held. Not surprisingly, it is strong attitudes that are more likely to predict your actions.

Needless to say, it is also essential that the attitude in question be related to the behavior at hand; trying to predict voting behavior from attitudes about ice cream would be a fool’s errand.

2. The More Specific the Attitude, the Better It Will Be as a Predictor

One of the problems with early attitude studies was that they often relied heavily upon the measurement of very broad attitudes such as whether a person was a liberal or a conservative, and then the researchers attempted to predict behavior in a very specific arena such as engaging in an anti-war protest or volunteering to work for a pro-environmental or anti-abortion activist organization.

However, the usefulness of broad attitudes for predicting specific behaviors is limited. For example, one study found that using a general attitude toward birth control to predict which women were likely to use birth control pills did not work very well, but measuring a much more specific attitude toward birth control pills did indeed provide good predictive power. Similarly, another study demonstrated that attitudes about recycling are much better predictors of recycling behavior than attitudes about environmental issues in general.

The necessity of measuring attitudes as specifically as possible has been confirmed in literally hundreds of other studies .

3. Attitudes Are More Useful as Predictors When They Are “Accessible” to the Person

Sometimes, people are just not aware of the attitudes that they hold when they are engaging in a behavior that is relevant to some of those attitudes. Dragging awareness of these attitudes into consciousness enables them to kick in as a force that drives behavior.

Several studies by University of Minnesota psychologist Mark Snyder and his colleagues illustrated just how important this can be. In one case , he found that favorable attitudes by college students toward psychology experiments did not predict actually volunteering to participate in experiments unless the students were reminded of the attitude they had expressed when a call for volunteers was made. In a separate study , students posing as jurors in a mock sex - discrimination case were more likely to render verdicts consistent with their attitudes when they were given an opportunity to reflect on their attitudes about affirmative action prior to making a judgment.

4. Attitudes Are Better Predictors of Behavior for Some Individuals Than for Others

There are a number of personality traits that may lead some individuals to be guided by their attitudes more reliably than others.

For example, some people are simply higher in something called “ Private Self-Awareness .” Such individuals are more introspective and are constantly monitoring and examining their thoughts and feelings. Consequently, a person who scores high in private self-awareness will be more conscious of the attitudes that they hold and they will be more attentive to whether their attitudes and their behaviors are in synchrony with each other. We will naturally have more confidence in predicting behavior from an attitude when we are dealing with a highly self-aware person.

5. Attitudes Are Better Predictors of Behavior When the Elapsed Time Between Attitude Assessment and the Actual Behavior Is Short

A political poll taken two days before an election will be more accurate than a poll that was taken two months before an election. The longer the amount of elapsed time, the more likely it is that other forces may intervene to alter the impact of the attitude.

6. Attitudes Predict General Patterns of Behavior Over Time Better Than Single Instances of Behavior

Another problem with some of the early attitude research was that it often measured an attitude and then used it to predict a person’s behavior in a single experimental session. But we now know that attitudes are better at predicting long-term behavior patterns.

Suppose that I discovered in an attitude survey that you are an environmentally conscious person who believes in recycling. Now, imagine my surprise when I see you casually toss an empty aluminum soda can into the trash rather than saving it to recycle later.

In a single situation, there are simply too many other factors that might be at play for your attitude to be the primary determinant of what you do. Powerful other forces such as being in a hurry to get to a job interview or the time costs involved with searching for a recycling container may overpower even the most sincerely held attitudes and cause you to behave in ways that are totally out of step with your beliefs.

On the other hand, if I were to monitor your daily recycling behaviors over the next six months, I would likely see the influence of your attitudes coming through more often than not and discover that you are in fact a pretty reliable recycler.

In short, the evidence is that attitudes can be good predictors of behavior—as long as we understand the constraints that they operate under. Predicting behavior over longer periods of time and in precise situations will be best, especially if we are using very specific attitudes that are stable and held with conviction.

Frank McAndrew, Ph.D., is the Cornelia H. Dudley Professor of Psychology at Knox College.

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Attitude theory and measurement in implementation science: a secondary review of empirical studies and opportunities for advancement

• Jessica Fishman   ORCID: orcid.org/0000-0003-2867-8166 1 ,
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Implementation Science volume  16 , Article number:  87 ( 2021 ) Cite this article

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A Correction to this article was published on 23 May 2022

A Letter to the Editor to this article was published on 10 May 2022

This article has been updated

Implementation science studies often express interest in “attitudes,” a term borrowed from psychology. In psychology, attitude research has an established methodological and theoretical base, which we briefly summarize here. We then review implementation studies designed to measure attitudes and compare their definitions and methods with those from psychology.

A recent review identified 46 studies empirically examining factors associated with implementation. For each of these studies, we evaluated whether authors included attitudes as a construct of interest, and if so, whether and how the construct was defined, measured, and analyzed.

Most of the articles (29/46 [63%]) mention attitudes as an implementation factor. Six articles include a definition of the construct. Nineteen studies were designed to measure attitudes but lacked clarity in describing how attitudes were measured. Those that explained their measurement approach used methods that differed from one another and from validated methods in social psychology. Few articles described associated analyses or provided results specific to attitudes. Despite the lack of specificity regarding relevant measurement, analysis, and results, the articles often included causal conclusions about the role of attitudes.

Conclusions

Attitudes may be an important construct to implementation scientists, but studies to date are ambiguous in their definitions of attitudes and inconsistent in the methods used to measure and analyze attitudes. We discuss how implementation studies can apply psychology’s standardized definitions, validated measurement approaches, and causal models that include attitudes. This application of attitude theory and methods could offer implementation research valuable scientific opportunities.

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Contributions to the literature

Implementation science often posits that attitudes are an important factor influencing implementation. Yet, few studies apply standardized practices from attitude theory or measurement.

We document conceptual ambiguity and use of methods that are inconsistent and also discuss how advances in attitude measurement and theory from social psychology can help implementation science (1) establish a common approach with shared language, (2) pool data from multiple studies, (3) estimate the predictive validity of attitudes, and (4) identify when effective implementation strategies are likely to be those that target attitudes.

Within every scientific discipline, scientists strive to develop both uniform definitions and measurement methods for important constructs. Shared terminology and standardized methods enable scientists to collectively advance relevant theory by testing relationships among variables of interest [ 1 ]. Implementation science has been described as “somewhat elusive and overwhelming for researchers” because it has not yet developed distinct construct definitions and associated psychometrically sound instrumentation [ 2 ]. These challenges can be observed in the use of the term “attitudes” and its associated measurement in implementation studies. Implementation scientists’ interest in attitudes is evident from some of their earliest and most prominent measures and frameworks. However, it is not clear whether implementation scientists are using a common definition of attitudes or standardized measurement. It is also unclear whether their definitions and measurement are consistent with validated approaches commonly used in social psychology.

Implementation science is a relatively new field that seeks to build on many existing disciplines, especially organizational, industrial, and social psychology. Attitude research has historically been a product of psychological research, but is also relevant to implementation science’s explorations of how cognition can influence behavior in a professional context — specifically, whether individuals in an organization use evidence-based practices [ 1 , 3 , 4 ]. In this review, we provide a brief three-part summary of psychology’s advances in attitude research that may be relevant to implementation science. We then review recent implementation studies to examine their consistency with one another and psychological research. Finally, we offer suggestions for implementation scientists who wish to measure attitudes towards the use of evidence-based practices in their research.

Defining attitudes

The psychologist Herbert Spencer is credited with first using the term “attitude” in 1862 [ 5 ]. In the early twentieth century, Gordon Allport declared that the concept of attitude is “probably the most distinctive and indispensable concept” in psychology [ 6 ]. Throughout much of the twentieth century, psychologists debated its meaning; the debates waned only in the last few decades of that century [ 7 , 8 ]. Previously, a wide variety of concepts were labeled “attitude” [ 9 , 10 ]. The lack of distinction limited the discipline’s ability to understand attitude in terms of its relationships to other constructs [ 11 ].

In the 1920s, Louis Thurstone and others argued that the distinctive feature of attitude was an evaluative or affective predisposition towards an object, idea, or issue [ 12 ]. Thurstone is credited with developing a formal technique for examining attitudes. To indicate one’s attitude towards an issue, he paired statements with numerically scaled response options so he could calculate the degree to which one judged that issue favorably or unfavorably. In line with Thurstone, major mid-century social psychologists like Martin Fishbein and Icek Ajzen conceptualized an attitude towards a behavior as an evaluative response that predisposed one favorably or unfavorably towards performing that behavior [ 9 ]. For example, if one believes that performing a behavior has mostly positive consequences, then one’s attitude would be supportive (or in favor) of performing that behavior. In contemporary psychology, one’s attitude towards a behavior is generally still defined as the degree to which one has a positive versus a negative evaluation of performing the behavior [ 8 , 10 , 13 ].

Applied to implementation research, an attitude would be conceived as how favorably or positively one is predisposed towards using a particular evidence-based practice (EBP). This predisposition is established by one’s beliefs about the consequences or outcomes of using that EBP, which may be viewed as an advantage or disadvantage of taking that action. For example, practitioners’ attitudes towards using an EBP may be based on whether they believe that using the EBP will be relatively pleasant versus unpleasant, necessary versus unnecessary, beneficial versus harmful, or simply good versus bad [ 14 ].

Attitudes in causal models

Throughout much of the twentieth century, psychologists debated whether attitudes could predict behavior [ 9 , 15 , 16 , 17 ]. Early results were disappointing [ 9 ]. However, advances in psychology have led to the general consensus that attitudes towards a behavior can predict behavior by way of a meditator: behavioral intention, which is the strongest predictor of behavior [ 18 , 19 ]. Fishbein and Ajzen pioneered the study of behavioral intention, describing it as the volitional and immediate antecedent of behavior. The construct is used to represent the subjective probability that one will perform a given behavior, as well as the amount of effort one is likely to exert [ 19 , 20 ]. In implementation research, the intention to use an EBP has been shown to predict its use [ 1 , 21 ].

According to several causal models, behavioral intention is a function of attitudes and other psychological variables, such as subjective norms and self-efficacy [ 22 ]. Experimental and observational data support these relationships, and the proposed causal pathways predict behavior [ 19 ]. However, some types of attitudes are much more important in predicting behavior than others.

Attitudes toward an object, person, policy, or concept are unlikely to predict behavior [ 13 , 19 ]. For example, attitudes toward doctors or health care policy or even health and disease will not predict whether one receives flu vaccinations, cancer screenings, or other evidence-based medical interventions [ 13 , 19 ]. When the goal is to predict a behavior (including use of an EBP), it is important to study attitudes towards the specific behavior of interest, which may have a decisive influence on one’s strength of intention to perform that behavior and, in turn, one’s actual performance of that behavior [ 9 , 19 , 23 ]. As discussed below, this theoretical advance is reflected in standardized measurement approaches.

Advances in attitude measurement

Throughout the twentieth century, the field of psychology explored hundreds of different quantitative measurement procedures. Oftentimes a procedure was unique to one study and lacked justification other than the investigator’s intuition, leading to “conflicting results and different conclusions concerning the relations between attitude and other variables” [ 24 ].

Psychology has since standardized methods for measuring attitudes, namely the Thurstone and Likert attitude scales [ 9 , 10 , 25 ]. Psychologists typically measure the degree to which one has a positive versus a negative evaluation of the behavior using a set of bipolar semantic differential scales [ 8 , 24 , 25 , 26 ]. Bipolar adjectives may include pleasant-unpleasant, wise-foolish, beneficial-harmful, necessary-unnecessary, useful-useless, and, more simply, good-bad [ 24 , 25 , 26 ]. To measure therapists’ attitudes towards using a particular EBP at each patient session, the therapist could rate how beneficial versus harmful and pleasant versus unpleasant it would be for them to do so [ 3 ]. These adjectives usually anchor a 5- or 7-point scale. The responses are aggregated and used to assign a respondent a single number representing how favorably or unfavorably the individual regards a behavior. There are several detailed guidelines on scale construction and analyses [ 14 , 19 , 26 , 27 ].

Psychologists have also developed validated, qualitative methods of assessing the beliefs that underlie attitudes. Standardized belief elicitation studies ask individuals what they believe to be the advantages and disadvantages of performing a behavior [ 28 , 29 ]. This literature also developed validated procedures for analyzing qualitative data and using it to tailor quantitative assessments of attitudes, based on the population of interest. Applied to implementation research, investigators conducting a belief elicitation would ask a practitioner to share what they believe to be an advantage or disadvantage (or good or bad) about using a particular EBP in the recommended context [ 30 , 31 ].

When assessing attitudes qualitatively or quantitatively, if the ultimate goal is to predict behavior, it is important to measure attitudes towards that specific behavior of interest rather than a general group of behaviors, a concept, policy, person, or object [ 24 , 25 , 26 ]. This measurement advance is referred to as the principle of correspondence; Presseau et al. have recently highlighted its applicability to implementation science [ 23 ]. Fishbein demonstrated that when behavior is defined as taking a certain action in a certain context, attitude items should refer to the same action in the same context; attitudes will vary depending on the behavior specified [ 23 ]. For example, when studying therapists’ implementation of CBT, therapists may have different attitudes depending on the CBT component that is of interest and the clinical context for using that CBT component [ 3 ]. For example, therapists’ attitudes towards using exposure therapy with children can differ from their attitudes towards using agenda setting or giving homework for the same population. Therapists’ attitudes towards using exposure therapy with children may also differ from their attitudes towards using exposure therapy with adults.

To our knowledge, this study represents the first attempt to compare attitude measurement and theory not only among implementation studies but also with definitions and methods from the field of psychology. Our goal was to examine the degree to which approaches can vary, because variation reveals opportunities to adopt shared definitions and apply validated, standardized methods. This review is part of a broader ongoing effort to increase the use of validated methods and theory among implementation studies. We therefore build on the work of others who have highlighted many ways that social psychology has contributed and can further contribute to implementation science [ 32 , 33 ].

Identifying articles for review

In 2020, Lewis et al. published a review of recent implementation studies that included empirical assessments of mediators, moderators, and mechanisms [ 34 ]. Their search identified 46 published studies. For three reasons, we selected these 46 studies for our analyses. First, this review was published in the field’s flagship journal, suggesting that it was judged to be a rigorous and comprehensive review. Second, because implementation science has been described as a rapidly evolving field, we sampled only articles published relatively recently [ 32 , 33 ]. Older articles may not reflect pertinent advances made in implementation science. For example, Godin et al. [ 35 ] identify several studies that reference attitudes; however, these are much older publications, which may not represent current practices in implementation science. Presumably, the more recent articles included by Lewis et al. were able to benefit from relevant earlier research and represent the field’s current practices.

Third, given that a chief objective of ours was to examine how attitudes are measured, an ideal sample is one in which articles are likely to include empirical assessments of attitudes. In psychology, attitudes are examined as mediators, and all the articles in our sample include some empirical assessment of a mediator, moderator, or mechanism. Other types of articles could mention attitudes but might be much less likely to measure attitudes empirically. To illustrate, a review by McIntyre et al. suggests that empirical analyses of this kind are very rare [ 36 ]. They reported that just seven out of 123 articles conducted empirical assessments of a theory-based construct (such as attitudes). We reviewed these seven studies and found that three did not study attitudes.

To qualify studies for inclusion in their review, Lewis et al. searched the PubMed and CINAHL Plus databases specifically for studies published in English that qualified as empirical implementation studies and involved the quantitative or qualitative exploration of mechanisms, mediators, or moderators. Lewis et al. systematically identified such articles that were published between January 1990 and August 2018. For the reasons noted above, this sample was not designed to be representative of the wide-ranging collection of existing implementation research.

Data collection

For each article in our sample, two coders extracted data. Inter-rater reliability was high for extraction of each type of data and coding (with coefficients of .90 or greater for Krippendorff’s alpha or Cohen’s kappa, and average percentage agreement of 94%, where appropriate); any discrepancies were resolved by consensus among authors. First, coders determined if the term “attitude” was used at least once in the text of the article. Among those that did, the coders reviewed if and how the authors defined and operationalized the term. Coders also tracked whether the authors assessed attitudes qualitatively or quantitatively, and how they measured attitudes, including whether attitudes were measured towards a behavior, such as the use of a particular EBP. The coders also recorded whether a study examined attitudes exclusively or in conjunction with other variables. Lastly, coders determined if the authors explained why they were studying attitudes (as opposed to other constructs). For this purpose, we tracked whether a validated model of behavior prediction informed their study of attitude, or if they otherwise justified their interest in this variable.

Defining “attitudes”

Of the 46 empirical implementation studies reviewed, 29 [63%] include the term “attitude.” Among these 29 articles, 6 include a definition (or some conceptual description how the construct can be operationalized), as shown in Table 1 . These definitions were similar across articles, and to the definition commonly used in psychology. For example, Garner et al. [ 37 ] defined attitudes toward using an evidence-based practice as the “positive or negative evaluations of behavior.” Bonetti et al. [ 38 ] stated: “Attitudes towards the behaviour are proposed to arise from a combination of beliefs about its consequences (behavioural beliefs) and evaluations of those consequences (outcome evaluations).” Carrera and Lambooij [ 39 ] define attitudes as “the sum of (positive and negative) beliefs weighted by evaluations of those beliefs.” Each of these descriptions reflects the seminal attitude research of social psychologists Martin Fishbein and Icek Ajzen.

Among the other 23 articles, some authors implied that attitudes are related to “barriers” or “facilitators” of implementation. For example, Brewster et al. [ 40 ] present their interest in learning about “facilitators” and consider “attitudes” as a “facilitator” but not a “barrier.” Other studies seem to consider attitudes to be conceptually distinct from “barriers” and “facilitators” [ 38 , 41 , 42 ]. Yet others study “barriers” and “facilitators” to implementation without clarifying whether either term is related to their examination of attitudes.

A study by Michie et al. [ 42 ] sought to identify theoretical constructs important to EBP implementation and “simplify [them] into construct domains.” In a table, they identify one of the domains as both “beliefs about consequences” and “anticipated outcomes/attitudes.” For this domain, they list 13 “component” constructs that include “attitudes” and “beliefs.” Within this domain, the other component constructs are wide ranging: “punishment,” “incentives/rewards,” “sanctions/rewards,” “salient events,” “critical incidents,” “unrealistic optimism,” “contingencies,” and “threat.” The domain and its component terms are not defined. A related publication by these authors [ 43 ] proposes a refined “Theoretical Domains Framework” by presenting 14 components as the ideal number of theoretical domains and revises some of the labels.

Investigators that aimed to examine attitudes also report assessing other potentially related concepts, such as perceptions of “relevance” [ 37 , 42 ], “usefulness” [ 39 , 44 , 45 , 46 , 47 ], “acceptance” [ 39 ], and “appropriateness” [ 38 , 39 , 48 ]. In each case, it is unclear if the authors define or operationalize these terms differently from attitudes. For example, given that Carrera and Lambooij [ 39 ] and Bonetti et al. [ 38 ] offer a definition of attitudes that does not refer to “relevance” or “usefulness,” it could be assumed that the authors treat them as distinct concepts.

Data used to study attitudes

Among the 29 articles that mention attitude as an implementation factor, 10 were not designed to measure attitudes but do discuss attitudes when reviewing prior studies . For example, based on the implementation science literature, Williams and Glisson [ 49 ] conclude that “organizational culture has been empirically linked to clinician attitudes.” The remaining 19 articles reported on studies designed to analyze attitude data, but three of these articles do not indicate whether they used qualitative or quantitative data (Chou et al. [ 50 ]; Kauth et al. [ 44 ]; Michie et al. [ 42 ]). The 16 articles that explain their qualitative or quantitative data collection are described in the sections below.

Quantitative data

Twelve articles report that they used a quantitative measure of attitudes. Three of these studies [ 45 , 51 , 52 ] used a 15-item instrument, the Evidence-Based Practice Attitude Scale (EBPAS). EBPAS developers acknowledge that the instrument assesses other constructs, such as knowledge [ 53 ]. For example, EBPAS items rely on knowledge of "new types" of practices. It also is worth noting that the EBPAS items refer only to a vaguely defined behavioral goal, such as adopting “new practices.”

Nine studies used other instruments that reflect different conceptual approaches from the EBPAS and from one another. For example, Stockdale et al. [ 54 ] examined attitudes using the Emotional Exhaustion subscale of the Maslach Burnout Inventory, which asks participants to “indicate how frequently you experience each feeling or attitude” referenced by nine statements, including “I feel I’m at the end of my rope” and “I feel I’m working too hard on my job.” Edmunds et al. [ 45 ] report using another study’s unpublished “Clinician Demographics and Attitudes Questionnaire” in addition to the EBPAS, and it is not clear how these two approaches to measuring attitudes were reconciled. Beets et al. [ 55 ] adapted items based on Dane and Schneider’s [ 56 ] “assessment of quality of delivery.”

Other studies report using part of an existing instrument but do not describe which ones and whether they applied some kind of summary index. Some authors report measuring attitudes using their own quantitative approach. For example, Rohrbach et al. [ 48 ] described hypothetical scenarios about a character named Joe and asked, “Would most of your friends think what Joe did was OK?” Those authors used the dichotomous yes/no answers, along with other responses, to create a numeric scale.

A few measures resemble validated ones from social psychology [ 14 ]. Bonetti et al. [ 38 ] describe the methods used to empirically assess the beliefs that may underlie attitudes towards referring patients for lumbar X-rays; they also include some questionnaire items. For example, they state: “Three items that assessed behavioural beliefs that referring for a lumbar X-ray would result in a particular consequence (‘reassure the patient’, ‘allay my uncertainty’, ‘make me more confident about managing the patient’s symptoms’) were rated on a 7-point scale…” They also explain that they summed the scores, with higher values representing stronger attitudes in support of referring patients for lumbar X-rays.

Qualitative data

Four articles used qualitative methods to examine attitudes [ 39 , 40 , 57 , 58 ]. None clearly describe which data were considered relevant to attitudes or how the data were analyzed. There were no indications as to whether attitudes towards a behavior (i.e., the use of a certain EBP) were examined. The authors did make conclusions about attitudes that were attributed to empirical analyses. For example, Brewster et al. [ 40 ] argued that attitudes “changed over time” but did not explain which data were categorized as attitudinal or how attitude change was analyzed.

As another example, Carrera and Lambooij [ 39 ] state that they studied attitudes of physicians and patients, but it is not clear if questions were asked to identify attitudes towards performing a particular behavior. For the analyses, transcripts from focus groups were “analyzed by qualitative content analysis,” yet it is not apparent which physician or patient data were content analyzed as attitudes. The authors report: “We found that physician’s attitude enabled the use” of one practice “while it impeded the use of” another. They add that, among patients, the direction of the effect was the opposite, where implementation was said to have “affected their attitude.” The authors did not explain how qualitative data were analyzed to make these causal determinations. It is also unclear if Carrera and Lambooij consider the data on “acceptance” to be measuring a construct distinct from attitudes. Tables 2, 4, and 5 list “attitudes,” “perceived usefulness,” and “acceptance” as separate factors; however, these constructs are conceptually merged in the manuscript’s main text and it is not clear if these terms actually pertain to the same data.

Attitudes in relation to other variables

All 29 articles describe studies that were designed to examine attitudes and other constructs. Some authors refer to validated models of behavior prediction that specify relationships between attitudes and other constructs. For example, Bonetti et al. [ 38 ] and Presseau et al. [ 59 ] refer to the Theory of Planned Behavior, which represents the role of attitudes within a network of other psychological constructs connected by causal pathways. In line with this model, Bonetti et al. [ 38 ] note that behavioral intention is a function of attitudes, subjective norms, and perceived behavior control. The Theory of Planned Behavior was the most commonly mentioned causal model [ 37 , 38 , 39 , 41 , 42 , 49 , 51 , 55 , 60 ]. However, descriptions of this model were often missing or not consistent with how the developers of this model describe it.

Some studies described roles that attitudes might play that conflict with existing evidence. For example, Edmunds et al. [ 45 ] describe the “usefulness of EBPs” as being determined by attitudes towards EBP. Conversely, Carrera and Lambooij [ 39 ] write that attitudes are the result of beliefs that “are influenced by perceived usefulness, which is the degree to which an individual believes that usage would be beneficial, and perceived ease of use, or the degree to which an individual is convinced that usage would not be arduous.” This is contradicted by their “analytic framework” description, however, in which attitude is “influenced by social norm, or the impact of one’s social environment, and enabling conditions, which are objective factors in the environment that promote action.” Some authors propose that attitudes should be studied because they function as an “integrating mechanism” [ 40 ], a source of bias [ 57 ], or a “facilitator of acceptance” [ 39 ]. Most authors did not mention if or how attitudes may relate conceptually to the other constructs of interest.

We found that oftentimes when investigators study attitudes, they do not define this construct. As Martinez et al. observe, implementation science could benefit from “carefully defining” a construct of interest, “ideally based on existing theory or available definitions” [ 2 ]. We recommend adopting the standardized definition of an attitude that is widely used in social psychology. An explicit definition of attitude can inform procedures for measuring this construct [ 24 ]. This standard definition would distinguish attitudes from other constructs with which they often are conflated, including willingness, intention, and self-efficacy. Definitions would also help clarify whether investigators intend to distinguish attitudes from other terms, such as “acceptability,” “appropriateness,” and “barriers and facilitators.” Without clear definitions, there are many opportunities for investigators to lack agreement on the meaning of constructs. For example, when developing a “Theoretical Domains Framework,” participants who “possessed a good understanding of behaviour change theory” were asked to interpret the meaning of attitudes and various other terms that were not defined. They found different ways to interpret the meaning of these terms and their theoretical relationship to each other [ 43 ].

In the studies explored in the present review, authors rarely explain how attitudes were measured. When they do, their accounts suggest that attitudes were measured in unrelated ways among different studies. Research in social psychology has shown that, with fundamentally different approaches to measuring attitudes, “a study can lead to apparently conflicting results and different conclusions concerning the relations between attitudes and other variables” [ 24 ]. Implementation science has the opportunity to apply validated, standardized measures of attitudes with guidance tailored specifically to implementation researchers [ 14 ].

In particular, future studies can measure attitudes towards using a particular EBP in a specified context and then test the degree to which attitudes explain variance in a population’s strength of intention to perform that behavior [ 61 ]. As documented by a systematic review of implementation science, behaviors are rarely specified clearly [ 62 ]. In turn, Presseau et al. warned, “Despite half a century of guidance on behaviour specification, research is frequently published in which the behaviour is poorly specified” [ 23 ]. Presseau et al. have argued that implementation science could improve the measurement of theoretical constructs by specifying the behavior of interest. This guidance can be applied specifically to attitude measurement. The present review found that studies measured attitudes towards general categories, such as “new practices” or “evidence-based practices” rather than a specific behavior.

The conceptual and methodological problems documented by this review are similar to those that the field of psychology faced in the early part of the twentieth century, as encapsulated by the following statement by Fishbein and Ajzen: “In addition to a lack of agreement on the definition of attitude, different noncorrelated operations can be found for the same concept, and the identical operation is often given different conceptual labels” [ 15 ]. They added that, as a result of this conceptual and methodological neglect, attitude research has largely been “noncumulative and has failed to produce a systematically integrated body of knowledge.” Without standardized approaches, it is difficult for the research to develop a common scientific language, compare or pool findings across studies, or develop theories that can establish causal mechanisms of implementation [ 2 , 63 ].

The utility of causal models

There is a great deal of empirical evidence about the role of attitudes based on psychological studies of many different behaviors. The lessons learned from psychological research about behavioral prediction and change are directly applicable to implementation research. Implementation research examines behavior within organizations. The discipline of psychology includes organizational psychology, which has the same goal as implementation science [ 64 , 65 ]. Both fields are concerned with identifying the determinants of behavior. When citing Eccles et al. [ 66 ], Presseau et al. [ 59 ] summarize: “Behavioural science has systematically operationalized theories concerning determinants of behaviour and how they are associated with each other. This may be useful for understanding the mechanisms underlying implementation interventions designed to change clinicians’ behaviour.” Godin et al. [ 35 ] also observe: “The problem of understanding why healthcare professionals do or do not implement research findings can be viewed as similar to finding out why people in general do or do not adopt a given behaviour such as health-related habits.” They stress, “This has been extensively investigated, and social psychological theories have already demonstrated their value.” In contrast to causal models, “frameworks” (such as the Theoretical Domains Frameworks) rely on intuition to identify the domains and constructs that seem to be “the most suitable.” Such frameworks do not identify which domains or constructs have causal relationships with one another. Well-tested models, on the other hand, represent the results of empirical tests (spanning many decades) that demonstrate the predictive validity of specific constructs and their causal pathways, which allow studies to identify the mechanisms of behavior change and design interventions to target them.

Theories that have demonstrated predictive validity, such as the Theory of Planned Behavior [ 67 ], Unified Theory of Behavior [ 68 ], and the Integrated Model [ 69 ], are based on evidence that attitudes can influence behavioral intention. These theories also posit that, in addition to attitudes, perceived behavioral control (or self-efficacy) and subjective norms can influence behavioral intention. This proposition could be tested within implementation research to better understand the degree to which attitudes explain variance in a study population’s motivation to implement a particular EBP. Implementation science can also test which variables influence attitudes. By designing studies to evaluate theorized relationships between constructs in causal models, the results can inform the development of implementation strategies. Implementation strategies are likely to be effective and efficient if they target the malleable constructs that predict outcomes [ 1 ].

When testing a causal model, it is important to empirically establish the degree to which attitudes contribute, since the role attitudes play will vary depending on the population and specific behavior of interest. When attitudes explain a substantial proportion of variance, intervention strategies can be developed to change attitudes — which are malleable — and potentially increase EBP uptake. For example, in many studies, attitudes drive intention to a greater extent than do subjective norms [ 18 – 20 ]. In other cases, subjective norms are more influential than attitudes [ 13 , 70 , 71 ]. Depending on which variables are predicting intention to use an EBP, implementation strategies can be designed to target the most influential determinant of intention, and strategies that do not may be less effective and efficient [ 70 , 71 ].

When considering the limitations of the current study, it is important to note that the articles reviewed may not be representative of other implementation studies. Indeed, we purposefully selected a sample of articles that empirically studied mediators and moderators. As documented by McIntyre, implementation science has a high proportion of articles that express interest in studying theory-based constructs, such as attitudes, but forgo an empirical assessment. In a secondary analyses of the articles included in McIntyre’s review, we found that 4% (4/123) provided information on attitude measurement, and doing so was more common in our primary sample. Given the breadth of research in implementation science, this review is not intended to represent the wide-ranging variety of studies that could be sampled.

In addition, the present review was limited to recent articles. Given that our results are based on recent publications, the results may be less relevant to older implementation research. For example, a review by Godin et al. [ 35 ] identifies several articles that mention attitudes but these articles are not included in our sample. Future research could investigate older studies in implementation science to determine if attitude theory and measurement has improved over time.

This review found that implementation scientists demonstrate a considerable interest in attitudes as a construct. However, investigators have not yet adopted a standard definition of attitudes. In addition, their methods for measuring attitudes fundamentally differ, and this appears to reflect conceptual ambiguity or inconsistency. As others have observed, implementation research often lacks standardization for the conceptualization and measurement of constructs. But remedies are immediately available [ 1 , 35 , 66 ]. Over several decades, psychology has developed standardized definitions, along with measurement approaches that are supported by strong psychometrics, including predictive validity [ 18 – 20 ].

This review also found that the authors rarely articulated how attitude data were analyzed, and most did not present any empirical results labeled as attitudes. Few explained why attitudes matter, either in and of themselves or in relation to other study variables. Consequently, there are many missed opportunities for investigators to apply validated causal models of behavior that can inform the analytic plan and justify decisions to test the relationship between attitudes and other variables. Psychology has also generated a large literature documenting the role of attitudes within causal models of behavior, revealing a parsimonious set of constructs that are proximally influencing and influenced by attitudes [ 19 ]. Investigators studying implementation of evidence-based practices can evaluate theorized relationships between attitudes and other constructs in these predictive models [ 1 ]. Such tests could inform the development and evaluation of effective implementation strategies.

Availability of data and materials

The dataset supporting the conclusions of this article is available from the Penn ALACRITY Data Sharing Committee by contacting the research coordinator, Kelly Zentgraf, at [email protected] , 3535 Market Street, 3rd Floor, Philadelphia, PA 19107, in https://hosting.med.upenn.edu/cmh/people/kelly-zentgraf/

Change history

23 may 2022.

A Correction to this paper has been published: https://doi.org/10.1186/s13012-022-01204-9

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Fishman, J., Yang, C. & Mandell, D. Attitude theory and measurement in implementation science: a secondary review of empirical studies and opportunities for advancement. Implementation Sci 16 , 87 (2021). https://doi.org/10.1186/s13012-021-01153-9

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A meta-analysis (k of conditions = 128; N = 4,598) examined the influence of factors present at the time an attitude is formed on the degree to which this attitude guides future behavior. The findings indicated that attitudes correlated with a future behavior more strongly when they were easy to recall (accessible) and stable over time. Because of increased accessibility, attitudes more strongly predicted future behavior when participants had direct experience with the attitude object and reported their attitudes frequently. Because of the resulting attitude stability, the attitude-behavior association was strongest when attitudes were confident, when participants formed their attitude on the basis of behavior-relevant information, and when they received or were induced to think about one- rather than two-sided information about the attitude object.

• Attitude accessibility
• Attitude formation
• Attitude stability
• Attitude-behavior relation
• Meta-analysis

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AB - A meta-analysis (k of conditions = 128; N = 4,598) examined the influence of factors present at the time an attitude is formed on the degree to which this attitude guides future behavior. The findings indicated that attitudes correlated with a future behavior more strongly when they were easy to recall (accessible) and stable over time. Because of increased accessibility, attitudes more strongly predicted future behavior when participants had direct experience with the attitude object and reported their attitudes frequently. Because of the resulting attitude stability, the attitude-behavior association was strongest when attitudes were confident, when participants formed their attitude on the basis of behavior-relevant information, and when they received or were induced to think about one- rather than two-sided information about the attitude object.

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Module 5: Attitudes

Module Overview

An important part of how we think about ourselves and others comes from our knowledge of how we view the world. This view, as we have seen from previous modules, is shaped by our self-knowledge and the ways we think and perceive, which we saw are often filled with errors and biases. In this module, we are turn our attention to our attitudes. They are the final piece to understanding how we think about ourselves and others. This module will focus on what they are, why they are important – focusing on the predictive nature of attitudes and finally how our behavior can impact our attitudes.

Module Outline

5.1. What is an Attitude?

• 5.2. Why are Attitudes Important?
• 5.3. How does our Behavior Impact our Attitudes?

Module Learning Outcomes

• Describe an attitude.
• Explain why attitudes are important.
• Introduce behavior prediction models.
• Explain how our behavior impacts our attitudes.

Section Learning Objectives

• Define an attitude
• Examine the structure and function of an attitude
• Investigate the origins of attitudes

First, an attitude is our assessment of ourselves, other people, ideas, and objects in our world (Petty et al., 1997) Ask yourself, what do you think about Jenny in your social psychology course, your discussion board question that is due this week, or puppies and ice cream? Your responses to these questions are your attitudes toward them. You might respond with “Jenny is really nice and always helps her classmates” or “I hated the discussion board question because it was really boring”. For most people, their attitude responses toward puppies and ice cream would be positive. We will see in this section that attitudes are a bit more complex than these examples suggest.

5.1.1. Structure and Function of an Attitude         

The first way we can examine attitudes is through a “tripartite” model. It is often referred to as the ABC’s of attitudes and consists of three bases or components, affect, behavior, and cognition . Originally, researchers believed that everyone’s attitudes contained all three bases, but we now know that some attitudes do not contain all three, and some are even inconsistent with each other (Rosenberg et al., 1960; Miller & Tesser, 1986b). Let’s more closely examine what this means. When we express affect , we are sharing our feelings or emotions about the person, idea, or object. In the examples above, when we love or hate those are clearly our feelings about the attitude object. We can see the cognitive component as well. This involves our thoughts about the attitude object, they often look like opinions or facts that we hold. So, when we think Jenny is nice and always helps her classmates or the discussion board question is boring, these are the facts as we see it about the attitude object. The examples above do not contain a behavioral component. This would be actions that result from these thoughts and/or feelings. So, we could add that you might befriend Jenny, not put as much effort into your discussion board response, buy ice cream, and pet puppies.

Figure 5.1. Tripartite Model of Attitudes

For Further Consideration

Take a minute and think of some attitudes you hold. Write them down on a sheet of paper. You can use them throughout the module. Let’s start with the first couple you wrote down. Try to break them down into the ABC’s of attitudes. Start with affect (what are your feelings about the attitude you hold), cognition (thoughts about the attitude you hold), and behavior (actions you take because of the attitude).

In the above examples and the ones you practiced, you were assuming that the attitude contained all three bases. Again, we know that some attitudes are only made of one or two bases and we also know that they can be inconsistent (Millar & Tesser, 1992). An example might help us to understand – you might only have thoughts and feelings about puppies. You don’t have any actions connected to it. These thoughts and feelings might not line up. You might love puppies, but your thoughts are connected to how allergic you are to them and how much hair they shed, which will make your allergies worse. So, this can be a challenge for us later when we are trying to predict how you will behave around puppies. You love them, but you cannot be around them since they make you sick. Will you pet the puppy anyway? Will your affect base be stronger than your cognitive base? We need to know which one is more important, stronger or more powerful to predict your behavior (Rosenberg et al., 1960; Millar & Tesser, 1986b).

Functional theorists Katz (2008) and Smith, Bruner, & White (1956) addressed the issue of not knowing which base (affective, cognition or behavior) was most important by looking at how the person’s attitude serves them psychologically. They came up with four different functions that an attitude might serve. One of the most beneficial things an attitude can do for us is to make our lives more efficient. We do not have to evaluate and process each thing we come into contact with to know if it is good (safe) or bad (threatening; Petty, 1995). This is called the knowledge function, and it allows us to understand and make sense of the world. My attitude towards insects is somewhat negative. I tend to have large reactions to bites from them and although most do not bite, my immediate reaction is to avoid them if at all possible. In this way my attitude keeps me from having to evaluate every type of insect I come into contact with. Saving time and allowing me to think of other things in life (Bargh, et al.,1992). This example might have prompted you to think that this generalization could lead to discrimination, and you would be correct. In an attempt to be more efficient, I am not stopping and processing every insect I come into contact with and some insects are good (safe). We will discuss how this helps explain prejudice and discrimination in a later module.

The other three functions serve specific psychological needs on top of providing us with knowledge that allows us to make sense of our world.   Our attitudes can serve an ego-defensive function which is to help us cover up things that we do not like about ourselves or help us to feel better about ourselves. You might think cheerleaders are stupid or superficial to protect yourself from feeling badly that you aren’t a cheerleader. Here you defended against a threatening truth – you aren’t a cheerleader, which you want to be, and you boosted your self-image by believing that you are better than them – you are smart and complex. We can categorize some of our attitudes as tools that lead us to greater rewards or help us to avoid punishments. So, women might have developed an attitude that having sex with many partners is bad. This has both a knowledge function and a utilitarian function by helping women avoid the societal punishment of being called a slut and then seeking the reward of being the kind of girl that someone would take home and introduce to their parents. The final function centers around the idea that some of our attitudes help us express who we are to other people, value-expressive function. We see this a lot on social media. If you were to examine someone’s Facebook or Instagram page you would see that their posts are full of their attitudes about life and they intentionally post certain things so that people will know who they are as a person. You might post a lot of political things and people might see you as a politically engaged person, you might post a lot about the environment and people see that you are passionate about this topic. This is who you are.

Look at the attitudes you listed earlier. Can you identify what function they serve in your life? Most attitudes serve the knowledge function, but are they also serving the ego-defensive or the utilitarian or the value-expressive functions? Pick out an example for each one. Do you have social media? What does it say about who you are? How does it meet the value-expressive function of attitudes?

Understanding the structure and function of attitudes can be useful for us but it is also important to know how they form or why some seem to be more powerful in guiding our behavior. Often, attitudes are formed from our own unique life experiences. This is why you will find that people’s attitudes and the strength of those attitudes vary so widely. As students in this course you will often find people have strong attitudes about certain topics. You might be surprised when they hold an attitude that is so different from yours and wonder how that is possible. We all have unique experiences that will shape our attitudes, opinions, and ideas about the world. So, when someone expresses an attitude that is different from your own it is most likely they had an experience in their own life that shaped that attitude (Fazio & Zanna, 1978). It is also possible to form an attitude indirectly from other’s experiences. For example, children develop many of their initial attitudes by observing caregivers and sibling’s reactions to their world. If your Mom or Dad is afraid of spiders or insects, then often children will develop an attitude of dislike and fear. Research finds that when attitudes are formed from direct experiences in life, as with the above example of being bitten by a spider and having a bad reaction, rather than indirectly where your parents are scared of spiders, there is a stronger attitude and a resulting stronger connection to someone’s behavior. What this means is we will be able to better predict your behavior toward a spider with direct experience formation over indirect experience formation.

Why do you think that attitudes formed from direct experience have greater predictive power on behavior? Well, recall what you learned in the module on the self. You might remember our discussion of the self-reference effect . We know that anything that is connected to us will be easier to remember and come to mind more quickly. So, it makes sense that if it happened directly to us it comes to mind quicker than attitudes that come from things that we heard about or saw someone else experience. If we follow this line of thinking, then indirect attitudes that came from people connected to us vs. strangers we read about online, should be stronger. The associations that are closest to us will result in the strongest attitude formation. (Anderson, 1993).

5.2. Why Are Attitudes Important?

• Explore how attitudes influence social thought.
• Examine factors that influence an attitude’s predictability of corresponding behavior.

5.2.1. Attitudes Influence Social Thought

We research value attitudes because we believe that they strongly influence social thought and can predict what someone will do. We as humans like for our worlds to be predictable. We want to believe that knowing how someone thinks and feels about something will give us insight into how they process the information they take in, as well as what they do with it. We have seen with previous modules how the way we think influences behavior, and we know attitudes color how we perceive all the information that is funneled in our direction.

In the previous module we focused on how our beliefs can alter our behavior and other people’s behavior. For example, with the self-fulfilling prophecy, our judgment of another person can alter our behavior towards them, thereby influencing them to respond to our behavior by acting in a way that supports our initial judgment and fulfills their prophecy. Our attitudes are often used to guide our behavior (Bargh, et al.,1992).

5.2.2. Attitudes Can Be Predictive of Behavior

Let’s start with an example. Do you think it is important to be honest? Most people say yes. They do not want to be perceived as a liar. We need to be trusted in order to have successful interactions and relationships. Your strong attitude toward honesty should allow me to predict that you will tell the truth. Would I be accurate in my prediction? The answer is no. Some of you might already be thinking of situations when the most socially acceptable response is to lie. What if you are at a wedding and the bride asks you how the cake tastes? It tastes terrible. Will you tell her the truth? The norms (unwritten rules or expectations) of this situation are to make sure the bride has a great day, so most of us would lie to protect her feelings. This illustrates a great example of an attitude not being predictive of someone’s behavior. Let’s examine when and how someone’s attitude might be more or less predictive of their behavior.

5.2.2.1. Aspects of the situation – Situational constraints. Let’s first look at the situation. Like in our honesty example, it seems that there are some moments where our attitude cannot be expressed in our behavior. When there are situational constraints that come from social norms , or unwritten rules that guide our behavior, we find that people might not behave according to their true attitude. You might have an attitude that dressing comfortably is more important than how you look. There are a lot of situations that might keep you from expressing this attitude. Often, we have to wear certain types of clothes to work, church or other events.

5.2.2.2. Aspects of the situation – Time pressure. Time pressure is another aspect of the situation that impacts how predictive an attitude will be. In this case, it will strengthen the attitude-behavior connection. We know that under time pressure, when we are in a hurry, we use attitudes as a way to save on our cognitive resources. We do not have to process the situation which takes time. We can just use the shortcut of our attitudes. In this way attitudes are operating much like heuristics which you learned about in the last module. They allow us to act with very little thought. This is why in this situation, our attitudes will vary and likely result in a behavior that fits our attitude.

5.2.2.3. Aspects of the Attitude – Attitude strength. It isn’t just the situation that can impact the attitude-behavior connection. There are also aspects of the attitude itself that can strengthen the connection. The stronger the attitude the more likely we can predict someone’s behavior from their attitude. A strong attitude is one that has the power to impact our thoughts and behavior and is resistant to change and stable over time. The research on strong attitudes often finds quite a few strength-related attitude attributes. We are going to focus on a few of them: attitude importance, knowledge, accessibility, and intensity (Petty & Krosnick, 1995).

We have already learned that an attitude will be stronger when it comes from our direct experiences and if we are closer to these strength-related attitude attributes, we can see how they contribute to attitude strength. Strong attitudes are important to us or psychologically significant and the more important an attitude is, the stronger it will be (Petty & Krosnick, 1995). So, you can ask yourself questions like, “How personally affected am I by this attitude object? How much do I care about it?”. Can you think of something that means a lot to you? I care a lot about the issues that impact women. I grew up in a highly gender stereotyped household and that direct experience impacted me and made it important to me. I now feel strongly about equality between the genders.

As we learn more about our attitude it will grow stronger. Knowledge of that attitude is the second factor. This is all the information we have about the attitude object (Petty & Krosnick, 1995). To continue the example, I spend a lot of time reading books on feminism, study gender equality, teach about gender and become more knowledgeable about equality.

If you remember from Module 3, the self-reference effect indicates that something connected to us will be remembered easier and more quickly. This is important to the third factor that increases strength, accessibility. We know an attitude is strong when it comes to our mind more often and more quickly (Petty & Krosnick, 1995). We measure this by timing how long it takes you to think about an attitude in relation to an attitude object.

Most people have a strong reaction to the following picture:

This strong reaction is a good example of attitude intensity or the strength of the emotional reaction that is elicited from the attitude object. In this case, maggots tend to elicit a strong reaction of disgust. Strong attitudes aren’t just better at predicting behavior. They are also less likely to change over time. This will be important to us in the next module on persuasion.

5.2.2.4. Aspects of the attitude – Attitude specificity. Another way that we can increase the chances that an attitude will lead to a consistent behavior is to make sure that the attitude is more specific than general. For example, if I want to predict if you will attend church every Sunday (more specific), I can’t ask you how you feel about religion (more general). I need to ask your attitude about attending church every Sunday. You will notice that they are at the same level of specificity or are more specific than general. Typically, the more specific the attitude the better it will be at predicting the specific behavior.

If you wanted to know if people were planning to vote for a specific candidate in the current election, what attitude would you need to know about them to predict who they would vote for?

5.2.2.5. Behavior prediction models. The important distinction between general attitudes and behavior-specific ones is that behavior-specific ones allow us to better predict behavior. Fishbein and Ajzen (1975) introduced a model that would allow us, through someone’s evaluation of behavior (attitudes) and thoughts on whether other important people would do the behavior (subjective norms), to predict their intention to do behavior and then that intention would predict whether they actually end up making the behavior. For example, one study looked at whether people would cheat on their significant other (Drake & McCabe, 2000). First, we need to know their evaluation, positive or negative, toward cheating on their significant other. Then we need to know if important others in their life would cheat on their significant other. Both pieces of information determine their intention to cheat on a significant other. If they intend to cheat then we will expect to see when we look at their behavior that they will cheat on their significant other. This is the theory of reasoned action . Later Ajzen separated from Fishbein believing that another critical component was part of the model and missing from the original theory. This model became the theory of planned behavior and added perceived behavioral control (Ajzen, 2012). This component is much like self-efficacy discussed in a previous module and deals with your confidence in being able to engage in the behavior. So, if you look at our cheating example, Ajzen believed that you could meet all the conditions above intending to cheat, but still not cheat. He said that if you do not believe you can cheat because you do not have the opportunity (place to cheat, person to cheat with, do not think you can get away with it) that you will not cheat. This an example of perceived behavioral control.

Figure 5.2 Theory of Planned Behavior

5.3. How Does our Behavior Impact our Attitudes?

• Define self-perception theory.
• Define cognitive dissonance.

5.3.1. Our Behavior Can Make Us Aware of Our Attitudes

One way that our behavior impacts our attitudes is when it helps us to understand what we are feeling. Often throughout the day we will have moments of uncertainty or ambiguity about our evaluation of an object, person, or issue. We will look to our actions to determine what it is we are feeling, called self-perception theory . All of this happens outside of our awareness. It is only through discussing it in a psychology course that you might introspectively examine the process and realize that an uncertainty about your feelings or attitude about your favorite music can be cleared up by looking at your music library and realizing that both rap and alternative are equally your favorite. Most often though we are not actively engaged in introspection and this process occurs outside of our awareness through an automatic processing of facial expressions, body posture, and behaviors (Laird & Bresler, 1992).

One of my favorite studies in psychology because of the ingenious methodology helps exemplify this idea. Researchers had one group of participants place a pen in their lips, which would inhibit a smile, and another group of participants were asked to put a pen in their teeth, which would facilitate a smile. Both groups then watched a funny segment of a cartoon. The researchers predicted and found that participants in the teeth condition evaluated the cartoon as funnier than the participants who placed the pen in their lips. The thinking behind this is that a pen in your teeth makes the muscles around your mouth move into a smile and we should interpret our feelings as positive based on this facial expression (Strack, et al, 1988). In recent years, researchers have done variations of this experiment with rubber bands and other interesting methodologies and found similar results (Mori & Mori, 2009).

5.3.2 Our Behavior Can Conflict with our Attitudes

Sometimes as we move through our lives, we will realize that some behaviors we are engaging in do not fit with one of our attitudes or we will have two attitudes that we realize seem to contradict each other. This inconsistency or conflict results in an unpleasant feeling that we want to immediately get rid of or reduce, called cognitive dissonance . It is another instance of how a behavior impacts our attitudes and, in this case, could change it. An example of this would be if you toss a can or newspaper in the trash and you hold the attitude that recycling is important to saving the planet. You will probably immediately feel like you are a hypocrite, especially if someone else points it out. It is important to us to get rid of this feeling as quickly as possible.

We will do this in one of three ways and choose the one that requires the least effort. We can change our attitude or behavior . I can take the can out of the trash. This is probably the option that requires the least effort. The next option for reducing dissonance is to seek out new information that supports our attitude or behavior. A popular example here is that smokers who feel dissonance from their behavior and the research on smoking dangers will seek out information that this research is inconclusive or minimal. In our example, we might recall a recent article we read outlining the recycling of one person and showing that it does not change the overall picture of climate change. We leave the can and reduce our dissonance. The last option is called trivialization . This is where we make the attitude less important. We might decide that recycling isn’t as important to us and that it isn’t changing the world. However, something like reducing our plastic consumption is an important attitude to replace the dissonant one (Petty, 1995).

Can you think of the last time you felt this unpleasant feeling from conflicting attitudes or an attitude and behavior? This process often occurs outside of our awareness. It is again only in a psychology course and through the introspection process where we would consider situations with these inconsistencies and then try to remember how we reduced them. A popular classroom demonstration to help students experience cognitive dissonance has students report how they feel about things like helping the homeless, eating a certain number of fruits and vegetables, voting in elections, and exercising regularly. As you can imagine most people have favorable attitudes toward these behaviors. They are then asked whether they have engaged in these activities recently or in the last year. Most answer no and experience cognitive dissonance. Can you imagine yourself in this situation? Which reduction technique would you use? I imagine that for most students the easiest one is trivialization and they might say, ‘This is just a dumb activity that teacher is doing.’ However it is possible that some students went on to exercise more or volunteer at the homeless shelter or sought out information that you can still be healthy, a good person, or civically engaged without doing those four types of behaviors.

Module Recap

This module covered attitudes, what they are, their structure and function, where they come from, their importance in their predictive nature, and how our behavior can influence them. Our evaluations of the world around us play a powerful role in shaping our world and guiding us through it. It isn’t surprising that attitudes are one of the most popular topics in social psychology. We ended this module by talking about cognitive dissonance and found that it has the potential to lead to attitude change. As we move into the next part of the text on influence, we will start with a module on persuasion. This module will build on our knowledge of attitudes and exemplify how persuasive communication can also lead to attitude change.

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Research Article

Decisions among the Undecided: Implicit Attitudes Predict Future Voting Behavior of Undecided Voters

* E-mail: [email protected]

Affiliation Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America

• Kristjen B. Lundberg, 
• B. Keith Payne

• Published: January 29, 2014
• https://doi.org/10.1371/journal.pone.0085680
• Reader Comments

11 Aug 2014: The PLOS ONE Staff (2014) Correction: Decisions among the Undecided: Implicit Attitudes Predict Future Voting Behavior of Undecided Voters. PLOS ONE 9(8): e105655. https://doi.org/10.1371/journal.pone.0105655 View correction

Implicit attitudes have been suggested as a key to unlock the hidden preferences of undecided voters. Past research, however, offered mixed support for this hypothesis. The present research used a large nationally representative sample and a longitudinal design to examine the predictive utility of implicit and explicit attitude measures in the 2008 U.S. presidential election. In our analyses, explicit attitudes toward candidates predicted voting better for decided than undecided voters, but implicit candidate attitudes were predictive of voting for both decided and undecided voters. Extending our examination to implicit and explicit racial attitudes, we found the same pattern. Taken together, these results provide convergent evidence that implicit attitudes predict voting about as well for undecided as for decided voters. We also assessed a novel explanation for these effects by evaluating whether implicit attitudes may predict the choices of undecided voters, in part, because they are neglected when people introspect about their confidence. Consistent with this idea, we found that the extremity of explicit but not implicit attitudes was associated with greater confidence. These analyses shed new light on the utility of implicit measures in predicting future behavior among individuals who feel undecided. Considering the prior studies together with this new evidence, the data seem to be consistent that implicit attitudes may be successful in predicting the behavior of undecided voters.

Citation: Lundberg KB, Payne BK (2014) Decisions among the Undecided: Implicit Attitudes Predict Future Voting Behavior of Undecided Voters. PLoS ONE 9(1): e85680. https://doi.org/10.1371/journal.pone.0085680

Editor: Brock Bastian, University of Queensland, Australia

Received: May 29, 2013; Accepted: December 5, 2013; Published: January 29, 2014

Copyright: © 2014 Lundberg, Payne. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors have no support or funding to report.

Competing interests: The authors have declared that no competing interests exist.

Introduction

What does it mean when someone says they are undecided about how to vote just weeks before an important election? Do undecided voters truly have no clear preferences? Do they have preferences, but are still weighing all the information at hand? Or, do they simply lack conscious insight into what their preferences are? Regardless, undecided voters are hard to ignore. Less than two months before the 2012 U.S. presidential election, five percent of voters in swing states were undecided, and another 17% said they might change their minds [1] . These percentages are more than enough to decide most elections, making the prediction of undecided voters' behavior an important problem for pollsters and social scientists. Is it possible to predict the behavior of individuals who cannot predict their own?

Psychologists have recently suggested an innovative solution to this problem. Galdi, Arcuri, and Gawronski [2] proposed that a key to predicting the behavior of undecided voters lies in the distinction between explicit and implicit attitudes. Explicit attitudes are evaluations of topics that are consciously endorsed and voluntarily reported. Traditional self-report surveys, including the questions asked in polls, measure explicit attitudes. In contrast, implicit attitudes are spontaneous evaluations that are automatically evoked when encountering or contemplating an issue. Implicit evaluations come to mind whether or not they are endorsed as true. They may affect behavior even when their influence is unwanted.

Galdi and colleagues [2] proposed that explicit attitudes are stronger predictors of behavior for decided voters than undecided voters, but implicit attitudes are stronger predictors for undecided voters than decided voters. They reasoned that implicit attitudes may indirectly influence future voting behavior by biasing the processing of decision-relevant information. To the extent that people use this biased set of information in making a deliberate decision, implicit attitudes may predict eventual decisions even though respondents are undecided when the implicit attitudes are measured. Evidence for this hypothesis was reported by Galdi, Gawronski, Arcuri, and Friese [3] , who demonstrated that the implicit attitudes of undecided participants predicted selective exposure to information consistent with their implicit attitudes. Individuals who describe themselves as undecided may therefore have implicit attitudes that will ultimately lead to conscious preferences, but have not yet done so (see also [4] ).

There is additional reason to believe that implicit attitudes may predict behavior for undecided voters: Being “decided” or “undecided” is a metacognitive judgment that people make about their own decision processes. Models of explicit and implicit attitudes suggest that people are more likely to consider their explicit attitudes to be a valid basis for judgment [5] , [6] . Whereas explicit attitudes are experienced as conscious preferences or “considered opinions,” implicit attitudes tend to be experienced as “gut feelings” [5] , [7] . Further, those who consider their automatic associations to be a less valid source of information have been shown to exhibit weaker correspondences between explicit and implicit measures [8] , [9] , [10] , [11] .

We suggest that, when people indicate whether they have decided, they are more likely to base this judgment on their consciously endorsed (i.e., explicit) attitudes than on implicit attitudes. If explicit attitudes strongly favor one option over the other, then the respondent will claim to be decided, whereas if the explicit attitude is weak, unclear, or ambivalent, then the respondent will claim to be undecided. Based on this reasoning, explicit attitudes should be more predictive of behavior among decided voters than undecided voters. Introspection, however, may overlook implicit attitudes, especially if those implicit attitudes are considered a less trustworthy source of information by default. If so, then when people introspect about whether they have decided, they may focus on consciously endorsed attitudes and neglect implicit attitudes. Implicit attitudes would nonetheless be automatically activated and would still have the potential to influence behavior. Based on this introspective neglect hypothesis, implicit attitudes should be equally predictive of behavior among the decided and the undecided.

The introspective neglect account and the biased processing account make different predictions about whether implicit attitudes should predict behavior for decided voters after explicit attitudes have been accounted for. Under the biased processing account, implicit attitudes lead to deliberate decisions only indirectly, by biasing the formation of the deliberate decision. Once the biased information search has led to a deliberate decision, no direct effect of implicit attitudes is predicted over and above the effects of explicit attitudes. Both accounts thus predict that implicit attitudes should predict decisions among the undecided, but only the introspective neglect account predicts that implicit attitudes should be similarly predictive among decided voters after removing the effects of explicit attitudes.

Do Implicit Attitudes Predict Future Behavior of Undecided Voters?

The theoretical reasons that implicit attitudes might predict the behavior of undecided voters are clear, but the empirical evidence to date has been mixed. In this section, we review the results of the most relevant previous studies. One study by Arcuri and colleagues [12] examined implicit political attitudes in the 2001 Italian general election. This study found that implicit attitudes predicted voting intentions for both decided and undecided voters, consistent with the idea that voters neglect implicit attitudes when they assess whether they are decided. Based on this finding, implicit attitude measures should be useful in predicting the behavior of undecided voters even if the voters themselves do not yet know how they will vote. However, this study did not simultaneously account for explicit measures and, therefore, cannot speak to the unique predictive power of implicit measures.

A second study by Galdi and colleagues [2] compared implicit and explicit attitudes toward a politically charged issue in an Italian sample. In separate analyses for decided and undecided participants, this study found that explicit, but not implicit, attitudes were predictive of future choices among decided participants. In contrast, implicit, but not explicit, attitudes were predictive of future choices among undecided participants. This pattern suggests that voters rely on explicit attitudes when assessing whether they have decided, but when they are undecided they rely more on implicit attitudes. These analyses, too, suggest that implicit attitude measures should be especially useful in predicting the behavior of undecided voters. However, in multiple regression analyses that treated decidedness as a dummy-coded moderator variable, explicit attitudes were not moderated by confidence in predicting future choices, while implicit attitudes were. While this finding corroborates the account that implicit attitudes may be more predictive for undecided than decided voters, it also suggests that explicit attitudes may be equally predictive for both decided and undecided voters.

Finally, Friese and colleagues [13] examined the ability of implicit and explicit attitude measures to predict voting behavior in the 2008 presidential election in the United States and the 2009 parliamentary election in Germany. Across three sets of multiple regression analyses that treated decidedness as a moderator variable and examined implicit attitude measures separately, implicit attitudes were consistently predictive of voting behavior, and they were more predictive for decided than undecided respondents (in contrast to Galdi et al.'s [2] findings). However, the variance due to explicit attitudes and their interaction with decidedness was not removed in these analyses. Because explicit and implicit attitudes were strongly correlated in these samples ( r s between .52 and .72), implicit attitudes may have spuriously appeared to be more predictive among decided than undecided voters because of the shared variance with explicit attitudes.

Indeed, once explicit attitudes were added as predictors to the models, a different pattern emerged: In Study 1 (as reported in their Table 3), explicit attitudes toward U.S. presidential candidates were more predictive for decided than undecided voters, but implicit attitudes were no longer a significant predictor of the vote. In Study 2 (as reported in their Table 7), explicit attitudes toward German parliamentary candidates were more predictive for decided than undecided voters, while implicit attitudes were predictive of voting for both decided and undecided voters. Finally, a second analysis in Study 2 (as reported in their Table 5) examined explicit and implicit attitudes toward political camps rather than candidates. In this case, both explicit and implicit attitudes were predictive of voting for both decided and undecided voters.

Taken together, two out of the three critical analyses in Friese et al. [13] suggest that explicit attitudes may be more predictive for decided than undecided voters. Implicit attitudes were equally predictive for decided and undecided voters in all three analyses, but the effects of implicit attitudes were small in all analyses and non-significant in one. It was these small effects of implicit attitudes that led Friese and colleagues to question the utility of implicit measures in forecasting behavior among the undecided.

The Present Research

In this study we re-examine the roles of implicit and explicit attitudes in predicting voting behavior among decided and undecided voters. Like Friese and colleagues [13] , we studied actual voting behavior in a major national election (the 2008 U.S. presidential election). Whereas previous studies used convenience samples or opt-in samples, ours is the first to address these questions using a large nationally representative sample. We began our re-examination of the findings of previous studies [2] , [12] , [13] by modeling the relationships between implicit and explicit attitudes toward the presidential candidates, decidedness, and voting behavior ( Analysis 1 ).

Our study provided a second opportunity to test the relationships of interest by examining implicit and explicit racial attitudes ( Analysis 2 ). The nomination of Barack Obama in 2008 offered an unprecedented occasion to study the role of implicit and explicit racial attitudes in voting for a Black presidential candidate. We replicated the analyses of candidate attitudes using these racial attitude measures and explored for the first time whether implicit and explicit racial prejudice predicted voting differently for decided versus undecided voters.

Finally, we tested a hypothesis implied by the introspective neglect account ( Analysis 3 ). If metacognitions about whether one has decided are based on explicit but not implicit attitudes, then more extreme explicit attitudes should be associated with greater confidence. This explanation predicts a curvilinear relationship between explicit attitudes and confidence, with more extreme attitudes in favor of either voting option associated with high confidence. Such a curvilinear relationship between confidence and implicit attitudes is expected to be smaller or absent if implicit attitudes are neglected during introspection.

Our study used different measures of implicit attitudes and decidedness than previous studies. Whereas previous studies [2] , [12] , [13] measured implicit attitudes using the Implicit Association Test (IAT) [14] , our study uses the Affect Misattribution Procedure (AMP) [15] . Although the IAT and the AMP are both well validated implicit measures, past research has generally found the two measures to be only weakly correlated [16] , [17] . This may be, first, because the AMP measures affective responses to the stimuli presented as primes whereas the IAT is more likely to measure associations to the category labels. Second, the mechanism driving the AMP is assumed to be a misattribution of affect from the prime to the target, whereas the mechanism driving the IAT is assumed to be response interference. A third difference is that the measure of interest in the AMP is an evaluation, whereas in the IAT it is response times. Thus, the two measures are likely to differ for both psychological and technical reasons.

Additionally, the previous studies we reviewed measured decidedness with a binary measure in which respondents classified themselves as decided or undecided [2] , [12] , [13] . Our study utilized a continuous measure of confidence in one's vote. The two measures differ in that our item did not allow subjects to make the determination of whether they considered themselves to have decided. With a binary measure, two respondents with identical levels of confidence may classify themselves differently if they apply different thresholds. The continuous measure of confidence avoids the threshold problem and may, therefore, provide a more precise measure of how strongly participants feel about their decision.

The difference in measures across studies suggests that our results may not be directly comparable to existing findings, and the results should not be interpreted as a direct replication attempt. Nonetheless, the underlying psychological questions that we are addressing are the same. These new data are well positioned to provide additional evidence on the important question of whether (and why) implicit attitudes may be effective predictors of decisions among respondents who are as yet undecided.

In summary, the present study tested (1) whether implicit attitudes toward Barack Obama and John McCain predicted voting among undecided voters, (2) whether implicit racial attitudes predicted voting among undecided voters, and (3) whether confidence in voting decisions was more strongly associated with the strength of explicit than implicit attitudes. Together, these data shed new light on the utility of implicit measures in predicting decisions among the undecided.

Respondents and sampling.

In all analyses described, we used data from the American National Election Studies (ANES) 2008–2009 Panel Study. For this study, panel respondents were recruited by telephone using random digit dialing to participate in an Internet-based study. Individuals who lacked a computer or Internet access at home were provided them at no cost. Respondents were compensated for completing monthly Internet surveys from January 2008 through August 2009. All analyses utilized sampling weights in order to correct for unequal probabilities of selection and nonresponse bias, as well as the Taylor Series method of calculating sampling errors and conducting significance testing in order to account for the clustering of the sample. These design-consistent estimation procedures allow us to generalize our findings to the American electorate. For additional information on the panel study, its sampling and recruitment techniques, and its procedures for the calculation of weights, please see DeBell, Krosnick, and Lupia [18] .

Ethics statement.

Data collection for the ANES 2008–2009 Panel Study was performed by Knowledge Networks, Inc., under a contract with Stanford University and with approval from the Stanford University Institutional Review Board (IRB). Knowledge Networks conforms to the Code of Standards and Ethics for Survey Research of the Council of American Survey Research Organizations, and all participants provided informed consent prior to participation. Additionally, the University of North Carolina at Chapel Hill Non-Biomedical IRB determined this research to be exempt from review for human subjects research (#08-0805).

In this first analysis, we began our re-examination of the role of implicit attitudes among undecided voters by predicting voting behavior from implicit and explicit attitudes toward the presidential candidates.

Implicit candidate preference.

Implicit candidate preference was measured using the Affect Misattribution Procedure (AMP) [15] . The measure was administered in either September or October 2008 (date of completion was determined randomly for each respondent). Participants completed 48 trials, each of which began by presenting a fixation cue, followed by a photograph of either Barack Obama or John McCain presented for 75 ms, followed by the appearance of a randomly assigned Chinese ideograph for 250 ms. Finally, the ideograph was replaced by a visual mask composed of black and white dots in a random “noise” pattern. The mask remained on the screen until a response was made. Respondents were instructed to judge whether each ideograph was pleasant or unpleasant while avoiding influence from the photographs. Unintentional influence of the primes on judgments can be used to measure attitudes toward the candidates pictured. Previous research has shown that the procedure is a reliable and valid measure of implicit attitudes [15] , [19] , [20] . Implicit candidate preference was calculated by subtracting the proportion of pleasant judgments that followed photographs of Mr. McCain from the proportion of pleasant judgments that followed photographs of Mr. Obama ( α  = .95). Higher scores indicate a greater implicit preference for Mr. Obama.

Explicit candidate preference.

Explicit candidate preference was measured using two items assessing liking for Mr. Obama and Mr. McCain. Respondents were asked to indicate the extent to which they liked each candidate in a branching question (“Do you like John McCain [Barack Obama], dislike him, or neither like nor dislike him?”; then “Do you (dis)like him a great deal, a moderate amount, or a little?”). Liking for Mr. McCain was subtracted from liking for Mr. Obama, such that higher scores indicate a greater explicit preference for Mr. Obama. Explicit attitude measures were collected during the same survey wave as the implicit attitude measure.

Confidence regarding one's voting intention.

To assess whether respondents had decided about their vote, respondents were first asked for whom they thought they would vote in the election for president. After answering, they were then asked: “How sure are you of that?” Responses were made on a 5-point scale from “extremely sure” to “not sure at all.” We scored the item such that higher scores indicate greater confidence in one's voting intention. Confidence was measured in the same wave as that in which the attitude measures were collected.

Voting behavior.

Respondents were surveyed in November after the election and were asked to report whether they voted for president and, if so, for whom they had voted. For our analyses, responses were scored such that 0 indicated a vote for Mr. McCain and 1 indicated a vote for Mr. Obama. All other responses (i.e., those who did not vote for president or voted for a third-party candidate) were not included in our analyses.

Of those respondents who completed all measures of interest ( N  = 2,013), 52.5% indicated a vote for Mr. Obama (47.5% for Mr. McCain). Additionally, 60.2% indicated that they were “extremely sure,” 18.2% that they were “very sure,” 14.4% that they were “moderately sure,” 4.1% that they were “slightly sure,” and 3.1% that they were “not sure at all.”

For all analyses, continuous variables were standardized using z -scores prior to analysis. Additionally, because respondents were completing the predictors of interest in both September and October 2008, a dummy variable indicating date of administration was included in all analyses, coded as 0 (September 2008) and 1 (October 2008), but coefficients are reported only for the attitudes and confidence variables that are of interest for the hypotheses tested.

Model 1 in Table 1 features the results of a logistic regression analysis predicting votes for Mr. Obama (versus Mr. McCain) from explicit candidate attitudes and their interaction with confidence. As expected, as explicit preference for Mr. Obama increased, respondents were significantly more likely to vote for Mr. Obama ( B  = 4.530, SE  = .500, p <.001). The interaction between confidence and explicit candidate preference also was significant ( B  = 1.343, SE  = .326, p <.001), indicating that explicit attitudes were more predictive of voting behavior for those who were more confident about how they would vote.

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https://doi.org/10.1371/journal.pone.0085680.t001

The second model in Table 1 shows a parallel logistic regression analysis examining implicit attitudes. Respondents with greater implicit preference for Mr. Obama were significantly more likely to vote for him ( B  = 3.011, SE  = .330, p <.001). Further, the interaction between confidence and implicit candidate preference was significant ( B  = .608, SE  = .244, p  = .013), and the effect was in the same direction as for explicit attitudes. These results are consistent with Friese et al.'s [13] finding that, when modeled separately, both explicit and implicit attitudes were more predictive of voting behavior for decided than for undecided voters.

The results thus far examined explicit and implicit attitudes separately. However, explicit and implicit candidate preferences were highly correlated ( r  = .688, p <.001). As noted previously, this shared variance between explicit and implicit attitudes makes it important to investigate the unique effects of each measure when the other is statistically controlled. The third model in Table 1 shows that explicit ( B  = 3.718, SE  = .473, p <.001) and implicit ( B  = 2.179, SE  = .351, p <.001) candidate preferences each uniquely predicted voting.

Critically, the interaction between implicit candidate preference and confidence became non-significant ( B  = .362, SE  = .239, p  = .130). Implicit attitudes were predictive of voting behavior across the range of confidence. Further, the interaction between explicit candidate preference and confidence remained significant ( B  = 1.215, SE  = .324, p <.001), indicating that explicit attitudes were more predictive of voting behavior at higher levels of confidence. To illustrate the nature of these relationships between attitudes and confidence, we calculated the simple slopes (displayed in Figure 1 ) relating implicit and explicit candidate preference to voting probabilities separately for respondents at each of the five levels of confidence. As confidence decreased, the predictive validity of explicit attitudes fell sharply, but the change for implicit attitudes was slight and non-significant.

Probability of voting for Mr. Obama (1) versus Mr. McCain (0) as a function of candidate preference, confidence, and their interaction. Panel A: The association between explicit candidate preference and voting was moderated by confidence. Panel B: The association between implicit candidate preference and voting was not moderated by confidence.

https://doi.org/10.1371/journal.pone.0085680.g001

To further examine the significant interaction between explicit attitudes and confidence, we tested the significance of those simple slopes (reported in Table 2). For voters at the four highest levels of confidence, explicit attitudes were a significant predictor of voting. However, at the lowest level of confidence (i.e., “not sure at all”), explicit attitudes were no longer significantly associated with voting. Though the simple slopes tests for implicit attitudes should be interpreted with caution given that the interaction between implicit attitudes and confidence was non-significant, it is clear that implicit attitudes were a significant predictor of voting across the full range of confidence ratings.

https://doi.org/10.1371/journal.pone.0085680.t002

Further, at the two lowest levels of confidence, the simple odds ratios for implicit attitudes were larger than those for explicit attitudes. To provide a more concrete illustration of these effect sizes, we will translate the predicted odds ratios into predicted probabilities. For those at the lowest level of confidence (i.e., “not sure at all” of their vote), the baseline probability of voting for Mr. Obama was 0.591, which corresponds to an odds ratio of 1.443. An increase of a single standard deviation in explicit preference for Mr. Obama uniquely increased those odds by a factor of 1.222 (i.e., 1.443×1.222 = 1.763), which translates to a probability of 0.638, while the same increase in implicit preference uniquely increased those odds by a factor of 3.099 (i.e., 1.443×3.099 = 4.472), which translates to a probability of .817. In short, “not sure at all” respondents who were one standard deviation above the mean on explicit preference for Mr. Obama had a predicted 64% chance of voting for him compared to the predicted 59% chance for those at the mean, while those who were one standard deviation above the mean on implicit preference had a predicted 82% chance of voting for Mr. Obama. Similarly, for those at the next lowest level of confidence (i.e., “slightly sure” of their vote), increases of a single standard deviation in explicit preference for Mr. Obama uniquely increased the chances of voting for him from 59% to 84%, while the same increase in implicit preference uniquely increased those chances to 86%. In other words, among more undecided voters, implicit candidate preference seemed to be a stronger predictor of voting behavior than explicit candidate preference.

However, it also should be noted that, at lower levels of confidence, despite the apparent differences in the magnitudes of the implicit and explicit attitudes estimates, the 95% confidence intervals for the pairs of estimates overlap substantially. For example, the 95% confidence interval for the simple odds ratio for the implicit candidate attitudes of “not sure at all” voters is 1.053 to 9.120, an interval which contains the simple odds ratio (1.222) for the explicit candidate attitudes of those same voters. Given the inappropriateness of calculating simple effects for implicit attitudes in the absence of a significant interaction with confidence, we are reluctant to draw firm conclusions regarding this finding. Nonetheless, this result suggests that, for voters who are more undecided, implicit and explicit attitudes are equally predictive of voting behavior.

Controlling for party affiliation and political ideology.

In order to conduct a more conservative test, we repeated the Model 3 analysis with the inclusion of two explicit covariates: political party affiliation and political ideology. In Friese et al.'s [13] Study 2, when an additional explicit indicator of voting attitudes was included, all previously significant effects for implicit measures became nonsignificant. However, in our analysis, though both new covariates were significant predictors of voting behavior ( p s<.001), the same pattern of results still emerged. Both explicit and implicit candidate preference were significant predictors of voting behavior ( p s<.001), and explicit preference was moderated by confidence ( p  = .034), while implicit preference was not ( p  = .399). Additional information regarding these analyses is included in Table S1 .

Summary of Analysis 1

To summarize, as voters became less sure of their vote, explicit evaluations of the candidates became sharply less predictive of eventual voting. Implicit attitudes remained a significant predictor across the range of confidence, although the predictive effects of implicit attitudes decreased slightly. At high levels of confidence, explicit attitudes were a much stronger predictor of voting, but both explicit and implicit attitudes predicted voting independently. At low levels of confidence, implicit attitudes were slightly stronger predictors than explicit attitudes.

The same ANES data used for the candidate attitudes were also used for the examination of racial attitudes. In the analyses that follow, measures of voting behavior and confidence in the decision were identical to the analyses of candidate preferences described previously. Measures of confidence were selected from the wave that corresponded with the date of AMP administration and, as before, a control variable was included to indicate this factor.

Implicit prejudice.

Implicit attitudes toward Blacks were measured using the Affect Misattribution Procedure (AMP). Respondents completed the race AMP in either September or October 2008. Those who completed the candidates AMP in September completed the race AMP in October, and those who completed the candidates AMP in October completed the race AMP in September. The race AMP was administered according to the same display parameters used in the candidates AMP. Photographs of non-famous Black and White men, matched on attractiveness and perceived typicality of their racial group, were used as primes. Implicit prejudice was calculated by subtracting the proportion of pleasant judgments that followed photographs of Black individuals from the proportion of pleasant judgments that followed photographs of White individuals ( α  = .77). Higher scores indicate greater bias against Blacks (or in favor of Whites).

Explicit prejudice.

We used all available explicit prejudice items that were administered before the election. The items included: (1) sympathy for Blacks, (2) admiration for Blacks, (3) perceptions that Blacks have too much political influence, (4) warm feelings toward Blacks, and (5) warm feelings toward Whites. The first three items were collected in September and the two feelings items were assessed in October. We subtracted the two feelings items to create a relative preference for Whites versus Blacks. All explicit items were then standardized and averaged into a composite, with higher scores reflecting more negative attitudes toward Blacks ( α  = .63).

This sample differs only slightly from the one used in the previous analysis. Of those respondents who completed all measures of interest ( N  = 2,024), 52.9% indicated a vote for Mr. Obama (47.1% for Mr. McCain). Additionally, 61.1% indicated that they were “extremely sure,” 18.3% that they were “very sure,” 13.0% that they were “moderately sure,” 4.1% that they were “slightly sure,” and 3.5% that they were “not sure at all.”

Explicit and implicit racial attitudes were modestly correlated ( r  = .282, p <.001), consistent with prior research [19] , [21] . Table 3 presents the results of three binary logistic regression analyses predicting votes for Mr. Obama versus Mr. McCain from explicit and implicit racial attitudes and their interactions with confidence.

https://doi.org/10.1371/journal.pone.0085680.t003

When explicit attitudes were modeled separately (Model 1), respondents with more negative explicit attitudes toward Blacks were significantly less likely to cast a vote for Mr. Obama ( B  = −1.253, SE  = .115, p <.001). Explicit attitudes also displayed a significant interaction with confidence ( B  = −.273, SE  = .115, p  = .017), indicating that explicit prejudice was more predictive of voting among decided voters than undecided voters.

When implicit attitudes were modeled separately (Model 2), individuals higher in implicit prejudice were less likely to vote for Mr. Obama ( B  = −.417, SE  = .067, p <.001). However, there was no interaction between implicit attitudes and confidence ( B  = −.059, SE  = .068, p  = .388), indicating that implicit prejudice was predictive of voting among both decided and undecided voters. This finding replicates the results with implicit candidate attitudes when the variance due to explicit attitudes was removed. In the case of racial attitudes, implicit and explicit measures were only modestly correlated, making the shared variance less problematic. The next analysis examined the consequences of controlling for explicit attitudes.

When implicit and explicit attitudes were modeled simultaneously (Model 3), both explicit attitudes ( B  = −1.212, SE  = .120, p <.001) and implicit attitudes ( B  = −.250, SE  = .077, p  = .001) uniquely predicted voting. The interaction between implicit attitudes and confidence remained non-significant ( B  = .035, SE  = .075, p  = .641), and the interaction between explicit attitudes and confidence remained significant ( B  = −.257, SE  = .117, p  = .029). To illustrate the nature of these relationships between prejudice and confidence, we plotted the simple slopes relating implicit and explicit racial attitudes to voting probabilities separately for respondents at each level of confidence (displayed in Figure 2 ). Explicit prejudice had a larger overall predictive effect than implicit attitudes. However, only explicit attitudes were moderated by confidence.

Probability of voting for Mr. Obama (1) versus Mr. McCain (0) as a function of racial attitudes, confidence, and their interaction. Panel A: The association between explicit racial attitudes and voting was moderated by confidence. Panel B: The association between implicit racial attitudes and voting was not moderated by confidence.

https://doi.org/10.1371/journal.pone.0085680.g002

Additionally, to examine further the significant interaction between explicit prejudice and confidence, we tested the significance of those simple slopes ( Table 4 ). For voters at the four highest levels of confidence, explicit prejudice was a significant predictor of voting. However, at the lowest level of confidence, explicit prejudice was not significantly associated with voting. While the simple effects for implicit prejudice remain weaker than those for explicit prejudice at each level of confidence, they vary only slightly across the range of confidence (simple odds ratios from .705 to .798), suggesting that implicit attitudes remain a consistent predictor regardless of confidence level. (Simple slopes for implicit attitudes should be interpreted with caution because there was no significant interaction.) At lower levels of confidence, the simple odds ratios for both explicit and implicit attitudes are comparable (e.g., .624 for explicit versus .705 for implicit for those who were “not sure at all”).

https://doi.org/10.1371/journal.pone.0085680.t004

As we did before in Analysis 1 , we can provide a more concrete illustration of the effect sizes by translating the predicted odds ratios into predicted probabilities. For “not sure at all” voters in Model 3, a single standard deviation increase in explicit prejudice uniquely decreased the probability of voting for Mr. Obama from 56% to 44%, while a single standard deviation increase in implicit prejudice decreased that probability to 47%. Similarly, for “slightly sure” voters, increases in explicit prejudice uniquely decreased the probability of voting for Mr. Obama from 54% to 37%, while increases in implicit prejudice decreased that probability to 46%. In other words, among more undecided voters, explicit prejudice seemed to be a stronger predictor of voting behavior than implicit prejudice. However, it also should be noted that, at the lowest level of confidence, despite the apparent difference in the magnitudes of the implicit and explicit attitudes estimates, the 95% confidence intervals for the pair of estimates overlap substantially, suggesting that, for voters who are more undecided, implicit and explicit attitudes are equally predictive of voting behavior.

Summary of Analysis 2

Together, the results of the racial attitudes analyses are generally consistent with attitudes toward the candidates: Explicit attitudes were more predictive of voting for decided than undecided voters, but implicit attitudes were similarly predictive for undecided and decided voters.

One reason that implicit attitudes should predict behavior among the undecided is that when people introspect about whether they have reached a decision, they attend primarily to consciously endorsed attitudes and neglect implicit attitudes. This does not imply that implicit attitudes are necessarily unconscious, but simply that people tend to consider them to be a less valid basis for judgments [5] , [7] . To test the hypothesis more directly, we next examined the relationships between attitude extremity and confidence. We expected that more extreme explicit attitudes would be associated with greater confidence in one's vote, but that this association would be weaker for implicit attitudes. Statistically, this hypothesis predicts a curvilinear relationship between explicit attitudes and confidence.

Candidate Attitudes

To test this hypothesis, we regressed confidence on explicit and implicit candidate preferences and their quadratic terms (see Figure 3 , Panel A). While the linear effects for explicit attitudes ( B  = .023, SE  = .029, p  = .428) and implicit attitudes ( B  = .001, SE  = .032, p  = .985) were non-significant, both quadratic effects were significant. More extreme explicit attitudes in favor of either candidate were strongly associated with greater confidence ( B  = .401, SE  = .021, p <.001). A similar but much weaker relationship was observed for implicit candidate attitudes ( B  = .064, SE  = .017, p <.001). Importantly, the 95% confidence interval for explicit attitudes (.359 to .443) did not include the coefficient for implicit attitudes, indicating that the quadratic term for explicit attitudes was significantly greater than that for implicit attitudes.

Panel A: The quadratic relationship between confidence and explicit candidate attitudes was larger than the quadratic relationship between confidence and implicit candidate attitudes. Panel B: The quadratic relationship between confidence and explicit prejudice was larger than the quadratic relationship between confidence and implicit prejudice.

https://doi.org/10.1371/journal.pone.0085680.g003

Racial Attitudes

We conducted a parallel analysis for racial attitudes (see Figure 3 , Panel B). The linear effect of explicit prejudice was significant, indicating that higher explicit prejudice was slightly associated with lower confidence ( B  = −.142, SE  = .036, p <.001). More importantly, the quadratic effect of explicit attitudes was also significant, indicating that more extreme racial attitudes (either pro-Black or anti-Black) were associated with greater confidence in one's voting intention ( B  = .134, SE  = .028, p <.001). For implicit race attitudes, neither the linear effect ( B  = −.021, SE  = .027, p  = .437) nor the quadratic effect ( B  = .013, SE  = .011, p  = .227) were significant.

Summary of Analysis 3

These analyses are consistent with the hypothesis that, when people introspect about whether they have decided, they focus on explicit attitudes. As a result, more extreme explicit attitudes are associated with greater confidence. Implicit attitudes, in contrast, may be overlooked when people assess whether they have reached a confident decision, especially if they are considered a less trusted or valid source of information. This asymmetry between explicit and implicit attitudes provides new evidence for a potential mechanism that may help explain why implicit attitudes predict future behavior among undecided voters. If strong implicit attitudes have significant effects on behavior but small effects on assessments of confidence, then implicit attitudes should predict behavior even among undecided voters.

General Discussion

The idea that implicit attitude measures can predict the behavior of undecided voters has generated a great deal of interest, because it suggests that precursors of decisions can be detected before respondents feel they have made up their minds. This idea has also generated some disagreement over whether and when such effects might be expected, as some prior studies have found implicit attitudes to predict voting among undecided voters whereas others have not. We re-examined these ideas using a large nationally representative sample and a longitudinal design, with implicit measures of both candidate attitudes and racial attitudes. We found that when implicit and explicit attitudes were highly correlated, as in the case of attitudes toward political candidates, the role of implicit attitudes depended critically on whether explicit attitudes were statistically controlled. When explicit candidate attitudes were not controlled, implicit candidate attitudes were more predictive among decided than undecided voters. However, this finding appears to result from the shared variance between implicit and explicit candidate attitudes. When explicit candidate attitudes were controlled, implicit candidate attitudes were equally predictive for decided and undecided voters. The results for candidate attitudes and racial attitudes both provided evidence that implicit attitudes may predict the behaviors of voters who feel that they have not made up their minds.

We also explored the hypothesis that implicit attitudes might predict behavior even among the undecided because, when people introspect, they attend to consciously endorsed attitudes and neglect implicit attitudes. Consistent with this idea, we found that more extreme explicit attitudes were associated with greater confidence. This association suggests an explanation for why explicit attitudes lost so much of their predictive power at lower levels of confidence: Weaker explicit attitudes are both less predictive of behavior and more likely to generate metacognitive judgments of low confidence. The relationship between attitudes extremity and confidence was much weaker for implicit attitudes. We also found that this effect was much larger for candidate attitudes than racial attitudes. This difference may result because, when people assess their confidence in their vote, they are most likely to introspect about their feelings toward the candidates. However, the fact that a similar (albeit weaker) tendency was found for explicit prejudice suggests that people may also have consulted their explicit feelings toward Blacks in general.

This pattern may also be consistent with Galdi and colleagues' [2] , [4] biased processing hypothesis. By that account, confidence accrues to the extent that confirmatory evidence is available to support one's biased processing. Thus, the same biased processing that leads to deliberate decisions may also lead to higher confidence. If we had failed to find evidence that confidence was more strongly associated with explicit than implicit attitudes, it would have cast doubt on the introspective neglect hypothesis. These curvilinear relationships offer positive support for the introspective neglect hypothesis, but they do not distinguish between that account and the biased processing account. Further, though these results are suggestive, they do not provide direct evidence for the causal mechanism presumed to underlie the introspective neglect hypothesis, namely that implicit attitudes do not factor into metacognitive judgments of decidedness because they are considered a less valid source of information. Future research should directly test this causal hypothesis.

Theoretical Significance

The present results shed light on an important asymmetry between how explicit and implicit attitudes predict behavior. Some perspectives suggest that implicit attitudes may only predict behaviors that are spontaneous or difficult to control, whereas explicit attitudes predict deliberate behavior [22] . Other authors have pointed out that a number of different patterns have been documented in the literature, including additive effects, indirect effects, and interactive effects [23] . The present results are consistent with both indirect effects and additive direct effects. Indirect effects of implicit attitudes through explicit attitudes are suggested by the fact that controlling for explicit attitudes reduced the predictive effects of implicit attitudes. And yet, after controlling for explicit attitudes, we found consistent unique effects of implicit attitudes.

Both indirect and direct pathways are important for understanding the causal forces that shape voting and other political behavior. Indirect pathways suggest that implicit attitudes influence the thoughts and feelings that will eventually be explicitly endorsed, through mechanisms such as biased processing of confirmatory evidence [3] . Even when explicit attitudes are the proximal cause, they may be the result of earlier implicit processes. Indirect effects are potentially important evidence for the purpose of understanding such causal chains. Direct effects are also important, but they suggest different processes. For example, prominent theoretical models suggest that highly accessible attitudes may influence decisions by influencing how the options are construed even if respondents are motivated to deny those attitudes explicitly [24] . Such effects would appear in the present data as a direct effect of implicit attitudes. Both direct and indirect effects provide evidence toward understanding the multiple pathways by which implicit and explicit attitudes may influence consequential behavior.

Practical Significance

Do implicit measures add substantively to the ability to forecast election outcomes? Explicit attitude measures are simpler and easier to administer than implicit measures, so implicit measures would have to show a non-trivial effect beyond explicit measures to justify including them for some practical purposes. If we were simply interested in whether implicit measures improve election forecasts for all voters, then overall measures of goodness of fit can provide us with the necessary information. For example, Table 1 shows one such measure of goodness of fit, the percentage of correctly classified cases, for the analyses involving candidate attitudes, while Table 3 shows the same information for the analyses involving racial attitudes. In both sets of analyses, though implicit attitudes are significant unique predictors of voting behavior, the models including both explicit and implicit attitudes (Models 3) do not improve substantially on the models including just explicit attitudes (Models 1). The increase in the percentage of correctly classified cases is 2.4% for candidate attitudes and just 0.5% for racial attitudes.

However, we are particularly interested in whether implicit measures improve election forecasts for a subset of voters, those who are less sure of their vote, to which these overall measures of goodness of fit cannot speak. To better address this specific question, we turn to a comparison of the model-implied simple effects estimates for both explicit and implicit attitudes at lower levels of confidence. (See Tables 2 and 4 .) For candidate attitudes, the greater magnitude and significance of the simple effects estimates for implicit attitudes compared to explicit attitudes suggests that implicit attitudes may be more predictive than explicit attitudes among voters who are more undecided. However, the reverse is true of racial attitudes; in this case, explicit attitudes may be more predictive than implicit attitudes among voters who are more undecided. Further, in both sets of analyses, the confidence intervals constructed around the simple odds ratios lead to the conclusion that, at lower levels of confidence, implicit and explicit attitudes are equally predictive of voting behavior.

Overall, explicit attitudes were clearly substantial predictors of the vote. Taking all these results together, we believe that the data suggest that implicit and explicit attitudes are equally predictive for less confident voters. Nonetheless, these conclusions are drawn from models in which implicit measures showed substantial and unique predictive power after controlling for explicit attitudes. This is especially so among undecided voters, for whom explicit measures lose some of their predictive power. Therefore, including implicit measures in studies of voting behavior seems potentially useful to election forecasters.

Relationship to Past Research

A close look at the results of Friese et al. [13] shows that, in the three analyses in which they entered implicit and explicit attitudes simultaneously (reported in their Tables 3, 5, and 7), the interaction between implicit attitudes and decidedness was not significant, consistent with the present results. Their interpretations differed from ours, however, because their study found that, once explicit measures were included, implicit attitudes were not consistently a significant predictor of voting. That is, in some analyses they found that implicit attitudes predicted voting for neither decided nor undecided voters after explicit attitudes were controlled, whereas in other analyses they found significant but small effects of implicit attitudes for both decided and undecided voters. Overall, the qualitative pattern of results in the Friese et al. study and ours is quite similar. The main difference is that we found a consistent unique effect of implicit attitudes whereas they found a less consistent effect.

Several procedural differences could potentially explain the discrepancies between these studies. Both studies used large samples, but only our sample was representative of the population. The two studies used different implicit measures and different measures of decidedness. Given these procedural differences, the parallels between the conclusions are rather more striking than their divergences. These explanations are necessarily speculative, and future research should systematically compare procedures to clarify the conditions under which implicit attitudes are most and least predictive.

Our findings are consistent with the general proposal by Galdi and colleagues [2] that implicit attitudes may be valuable for predicting the behavior of undecided voters. Our findings were not entirely consistent, however, with the particulars of Galdi et al.'s hypothesis or with the mechanism that they proposed. Firstly, the two predictions that can be derived from Galdi et al.'s original work are (1) that implicit attitudes are more predictive of voting behavior than explicit attitudes for undecided voters; and (2) that implicit attitudes are more predictive of voting behavior for undecided than decided voters. On the first point, our analyses do not firmly support that conclusion. In fact, the body of evidence suggests only that implicit and explicit attitudes are equally predictive of voting behavior for voters who are more undecided. Further, our findings do not support the second point: In our analyses, the nonsignificant interactions between implicit attitudes and confidence indicate instead that implicit attitudes are equally predictive of voting behavior for voters who are more or less decided.

Further, Galdi and colleagues' [2] , [4] biased processing account implies that implicit attitudes only affect deliberate decisions indirectly, through the biased processing of subsequent information. By the time a person has gathered enough confirmatory evidence to feel confident in their decision, there should be no direct effect of implicit attitudes after controlling for the effects of explicit attitudes. This account thus predicts no unique effect of implicit attitudes among decided voters. However, we found that implicit attitudes were predictive among both decided and undecided voters and that, if anything, their effects were slightly stronger among decided voters. This pattern is consistent with the hypothesis that people neglect implicit attitudes and focus on explicitly endorsed attitudes when they introspect about their confidence in decisions. Our findings do not rule out the hypothesis that biased processing contributes to the downstream effects of implicit attitudes. Both biased processing and introspective asymmetries may play a role in explaining why implicit attitudes interact with confidence differently than explicit attitudes.

Earlier research analyzed the racial attitudes data from the ANES and found that explicit and implicit racial attitudes each uniquely predicted voting, although the effects of explicit prejudice were larger [25] , [26] . Two recent papers re-examined the role of implicit prejudice in the ANES data and concluded that, whereas the effects of explicit prejudice were large and important, the unique influence of implicit prejudice was not large enough to be politically consequential [27] , [28] . None of these previous studies, however, considered whether voters were decided.

The present study suggests an interesting new perspective on the relative impact of implicit and explicit attitudes. By the time implicit attitudes were measured in the ANES (September–October), most respondents had reached a confident decision. The analyses in the present article suggest that the relative importance of explicit and implicit attitudes will depend on the proportion of the population who has reached a firm decision. Though one conclusion may be that explicit prejudice is generally more consequential for voting, an alternative hypothesis is that, when undecided voters are included in greater numbers, the relative effect size of implicit attitudes should increase. Future research should test this hypothesis by measuring implicit and explicit attitudes earlier in elections when fewer respondents have reached decisions.

Implicit attitudes have been suggested as a key to unlock the hidden preferences of undecided voters. Past research, however, offered mixed support for this hypothesis. The present research found that, when the influence of explicit attitudes was controlled, implicit attitudes predicted voting as well for the undecided as for the decided voters. Implicit tests may offer a useful tool in forecasting elections, especially at early stages when many voters have yet to make up their minds.

Supporting Information

Results of logistic regression analysis predicting voting behavior from explicit and implicit candidate preference and confidence, while including two additional explicit indicators of voting attitudes. Predicting votes for Mr. Obama (1) versus Mr. McCain (0) from explicit and implicit preference for Mr. Obama (versus Mr. McCain) and their interaction with confidence. Controlling for date of attitude measures administration, political ideology, and party affiliation. Corresponds to Table 1 , Model 3 (see main manuscript for details). Political ideology and party affiliation were both assessed in October 2008 using 6-point scales ranging from extremely liberal to extremely conservative and strong Democrat to strong Republican , respectively. All continuous variables have been standardized using z -scores. N  = 1,977. Correctly classified cases = 93.2%. B : regression weight B (log odds); SE : standard error of the regression weight B ; Wald: Wald test statistic; OR: Odds ratio. Relative amount by which the odds increase (OR >1.0) or decrease (OR <1.0) when the value of the predictor is increased by 1 SD.

https://doi.org/10.1371/journal.pone.0085680.s001

Author Contributions

Conceived and designed the experiments: KBL BKP. Analyzed the data: KBL. Wrote the paper: KBL BKP.

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Chapter 4. Attitudes, Behavior, and Persuasion

Exploring attitudes, learning objectives.

• Define the concept of an attitude and explain why it is of such interest to social psychologists.
• Review the variables that determine attitude strength.
• Outline the factors that affect the strength of the attitude-behavior relationship.

Although we might use the term in a different way in our everyday life (e.g., “Hey, he’s really got an attitude !”), social psychologists reserve the term attitude to refer to our relatively enduring evaluation of something, where the something is called the attitude object . The attitude object might be a person, a product, or a social group (Albarracín, Johnson, & Zanna, 2005; Wood, 2000). In this section, we will consider the nature and strength of attitudes and the conditions under which attitudes best predict our behaviors.

Attitudes Are Evaluations

When we say that attitudes are evaluations, we mean that they involve a preference for or against the attitude object, as commonly expressed in terms such as prefer , like , dislike , hate , and love . When we express our attitudes—for instance, when we say, “I like swimming,” “I hate snakes,” or “I love my parents” —we are expressing the relationship (either positive or negative) between the self and an attitude object. Statements such as these make it clear that attitudes are an important part of the self-concept.

Every human being holds thousands of attitudes, including those about family and friends, political figures, abortion rights, terrorism, preferences for music, and much more. Each of our attitudes has its own unique characteristics, and no two attitudes come to us or influence us in quite the same way. Research has found that some of our attitudes are inherited, at least in part, via genetic transmission from our parents (Olson, Vernon, Harris, & Jang, 2001). Other attitudes are learned mostly through direct and indirect experiences with the attitude objects (De Houwer, Thomas, & Baeyens, 2001). We may like to ride roller coasters in part because our genetic code has given us a thrill-loving personality and in part because we’ve had some really great times on roller coasters in the past. Still other attitudes are learned via the media (Hargreaves & Tiggemann, 2003; Levina, Waldo, & Fitzgerald, 2000) or through our interactions with friends (Poteat, 2007). Some of our attitudes are shared by others (most of us like sugar, fear snakes, and are disgusted by cockroaches), whereas other attitudes—such as our preferences for different styles of music or art—are more individualized.

Table 4.1, “Heritability of Some Attitudes,” shows some of the attitudes that have been found to be the most highly heritable (i.e., most strongly determined by genetic variation among people). These attitudes form earlier and are stronger and more resistant to change than others (Bourgeois, 2002), although it is not yet known why some attitudes are more genetically determined than are others.

Table 4.1 Heritability of Some Attitudes

Our attitudes are made up of cognitive, affective, and behavioral components. Consider an environmentalist’s attitude toward recycling, which is probably very positive:

• In terms of affect: They feel happy when they recycle.
• In terms of behavior: They  regularly recycle their bottles and cans.
• In terms of cognition: They believe recycling is the responsible thing to do.

Although most attitudes are determined by affect, behavior, and cognition, there is nevertheless variability in this regard across people and across attitudes. Some attitudes are more likely to be based on feelings, some are more likely to be based on behaviors, and some are more likely to be based on beliefs. For example, your attitude toward chocolate ice cream is probably determined in large part by affect—although you can describe its taste, mostly you may just like it. Your attitude toward your toothbrush, on the other hand, is probably more cognitive (you understand the importance of its function). Still other of your attitudes may be based more on behavior. For example, your attitude toward note-taking during lectures probably depends, at least in part, on whether or not you regularly take notes.

Different people may hold attitudes toward the same attitude object for different reasons. For example, some people vote for politicians because they like their policies, whereas others vote for (or against) politicians because they just like (or dislike) their public persona. Although you might think that cognition would be more important in this regard, political scientists have shown that many voting decisions are made primarily on the basis of affect. Indeed, it is fair to say that the affective component of attitudes is generally the strongest and most important (Abelson, Kinder, Peters, & Fiske, 1981; Stangor, Sullivan, & Ford, 1991).

Human beings hold attitudes because they are useful. Particularly, our attitudes enable us to determine, often very quickly and effortlessly, which behaviors to engage in, which people to approach or avoid, and even which products to buy (Duckworth, Bargh, Garcia, & Chaiken, 2002; Maio & Olson, 2000). You can imagine that making quick decisions about what to avoid or approach has had substantial value in our evolutionary experience. For example:

• Snake = bad ⟶ run away
• Blueberries = good ⟶ eat

Because attitudes are evaluations, they can be assessed using any of the normal measuring techniques used by social psychologists (Banaji & Heiphetz, 2010). Attitudes are frequently assessed using self-report measures, but they can also be assessed more indirectly using measures of arousal and facial expressions (Mendes, 2008) as well as implicit measures of cognition, such as the Implicit Association Test (IAT). Attitudes can also be seen in the brain by using neuroimaging techniques. This research has found that our attitudes, like most of our social knowledge, are stored primarily in the prefrontal cortex but that the amygdala is important in emotional attitudes, particularly those associated with fear (Cunningham, Raye, & Johnson, 2004; Cunningham & Zelazo, 2007; van den Bos, McClure, Harris, Fiske, & Cohen, 2007). Attitudes can be activated extremely quickly—often within one-fifth of a second after we see an attitude object (Handy, Smilek, Geiger, Liu, & Schooler, 2010).

Some Attitudes Are Stronger Than Others

Some attitudes are more important than others because they are more useful to us and thus have more impact on our daily lives. The importance of an attitude, as assessed by how quickly it comes to mind , is known as attitude strength (Fazio, 1990; Fazio, 1995; Krosnick & Petty, 1995). Some of our attitudes are strong attitudes, in the sense that we find them important, hold them with confidence, do not change them very much, and use them frequently to guide our actions. These strong attitudes may guide our actions completely out of our awareness (Ferguson, Bargh, & Nayak, 2005).

Other attitudes are weaker and have little influence on our actions. For instance, John Bargh and his colleagues (Bargh, Chaiken, Raymond, & Hymes, 1996) found that people could express attitudes toward nonsense words such as juvalamu (which people liked) and chakaka (which they did not like). The researchers also found that these attitudes were very weak.

Strong attitudes are more cognitively accessible—they come to mind quickly, regularly, and easily. We can easily measure attitude strength by assessing how quickly our attitudes are activated when we are exposed to the attitude object. If we can state our attitude quickly, without much thought, then it is a strong one. If we are unsure about our attitude and need to think about it for a while before stating our opinion, the attitude is weak.

Attitudes become stronger when we have direct positive or negative experiences with the attitude object, and particularly if those experiences have been in strong positive or negative contexts. Russell Fazio and his colleagues (Fazio, Powell, & Herr, 1983) had people either work on some puzzles or watch other people work on the same puzzles. Although the people who watched ended up either liking or disliking the puzzles as much as the people who actually worked on them, Fazio found that attitudes, as assessed by reaction time measures, were stronger (in the sense of being expressed quickly) for the people who had directly experienced the puzzles.

Because attitude strength is determined by cognitive accessibility, it is possible to make attitudes stronger by increasing the accessibility of the attitude. This can be done directly by having people think about, express, or discuss their attitudes with others. After people think about their attitudes, talk about them, or just say them out loud, the attitudes they have expressed become stronger (Downing, Judd, & Brauer, 1992; Tesser, Martin, & Mendolia, 1995). Because attitudes are linked to the self-concept, they also become stronger when they are activated along with the self-concept. When we are looking into a mirror or sitting in front of a TV camera, our attitudes are activated and we are then more likely to act on them (Beaman, Klentz, Diener, & Svanum, 1979).

Attitudes are also stronger when the ABCs of affect, behavior, and cognition all align. As an example, many people’s attitude toward their own nation is universally positive. They have strong positive feelings about their country, many positive thoughts about it, and tend to engage in behaviors that support it. Other attitudes are less strong because the affective, cognitive, and behavioral components are each somewhat different (Thompson, Zanna, & Griffin, 1995). Your cognitions toward physical exercise may be positive—you believe that regular physical activity is good for your health. On the other hand, your affect may be negative—you may resist exercising because you prefer to engage in tasks that provide more immediate rewards. Consequently, you may not exercise as often as you believe you ought to. These inconsistencies among the components of your attitude make it less strong than it would be if all the components lined up together.

When Do Our Attitudes Guide Our Behavior?

Social psychologists (as well as advertisers, marketers, and politicians) are particularly interested in the behavioral aspect of attitudes. Because it is normal that the ABCs of our attitudes are at least somewhat consistent, our behavior tends to follow from our affect and cognition. If I determine that you have more positive cognitions about and more positive affect toward waffles than French toast, then I will naturally predict (and probably be correct when I do so) that you’ll be more likely to order waffles than French toast when you eat breakfast at a restaurant. Furthermore, if I can do something to make your thoughts or feelings toward French toast more positive, then your likelihood of ordering it for breakfast will also increase.

The principle of attitude consistency (that for any given attitude object, the ABCs of affect, behavior, and cognition are normally in line with each other ) thus predicts that our attitudes (for instance, as measured via a self-report measure) are likely to guide behavior . Supporting this idea, meta-analyses have found that there is a significant and substantial positive correlation among the different components of attitudes, and that attitudes expressed on self-report measures do predict behavior (Glasman & Albarracín, 2006).

However, our attitudes are not the only factor that influence our decision to act. The theory of planned behavior , developed by Martin Fishbein and Izek Ajzen (Ajzen, 1991; Fishbein & Ajzen, 1975), outlines three key variables that affect the attitude-behavior relationship: (a) the attitude toward the behaviour (the stronger the better), (b) subjective norms (the support of those we value), and (c) perceived behavioral control (the extent to which we believe we can actually perform the behavior). These three factors jointly predict our intention to perform the behavior, which in turn predicts our actual behavior ( Figure 4.2, “Theory of Planned Behavior” ).

To illustrate, imagine for a moment that your friend Sharina is trying to decide whether to recycle her used laptop batteries or just throw them away. We know that her attitude toward recycling is positive—she thinks she should do it—but we also know that recycling takes work. It’s much easier to just throw the batteries away. But if Sharina feels strongly about the importance of recycling, if her family and friends are also in favor of recycling, and if she has easy access to a battery recycling facility, then she will develop a strong intention to perform the behavior and likely follow through on it.

Since it was first proposed, the theory of planned behavior has grown to become an extremely influential model for predicting human social behavior. However, although it has been used to study virtually every kind of planned behavior, a recent meta-analysis of 206 articles found that this model was especially effective at predicting physical activity and dietary behaviors (McEachan, Conner, Taylor, & Lawton, 2011).

Figure 4.2 Theory of Planned Behavior, adapted by Hilda Aggregani under CC BY.

More generally, research has also discovered that attitudes predict behaviors well only under certain conditions and for some people. These include:

• When the attitude and the behavior both occur in similar social situations
• When the same components of the attitude (either affect or cognition) are accessible when the attitude is assessed and when the behavior is performed
• When the attitudes are measured at a specific, rather than a general, level
• For low self-monitors (rather than for high self-monitors)

The extent of the match between the social situations in which the attitudes are expressed and the behaviors are engaged in is important; there is a greater attitude-behavior correlation when the social situations match. Imagine for a minute the case of Magritte, a 16-year-old high school student. Magritte tells her parents that she hates the idea of smoking cigarettes. Magritte’s negative attitude toward smoking seems to be a strong one because she’s thought a lot about it—she believes that cigarettes are dirty, expensive, and unhealthy. But how sure are you that Magritte’s attitude will predict her behavior? Would you be willing to bet that she’d never try smoking when she’s out with her friends?

You can see that the problem here is that Magritte’s attitude is being expressed in one social situation (when she is with her parents), whereas the behavior (trying a cigarette) is going to occur in a very different social situation (when she is out with her friends). The relevant social norms are of course much different in the two situations. Magritte’s friends might be able to convince her to try smoking, despite her initial negative attitude, when they entice her with peer pressure. Behaviors are more likely to be consistent with attitudes when the social situation in which the behavior occurs is similar to the situation in which the attitude is expressed (Ajzen, 1991; LaPiere, 1936).

Research Focus

Attitude-Behavior Consistency

Another variable that has an important influence on attitude-behavior consistency is the current cognitive accessibility of the underlying affective and cognitive components of the attitude. For example, if we assess the attitude in a situation in which people are thinking primarily about the attitude object in cognitive terms, and yet the behavior is performed in a situation in which the affective components of the attitude are more accessible, then the attitude-behavior relationship will be weak. Wilson and Schooler (1991) showed a similar type of effect by first choosing attitudes that they expected would be primarily determined by affect—attitudes toward five different types of strawberry jam. They asked a sample of college students to taste each of the jams. While they were tasting, one-half of the participants were instructed to think about the cognitive aspects of their attitudes to these jams—that is, to focus on the reasons they held their attitudes—whereas the other half of the participants were not given these instructions. Then all the students completed measures of their attitudes toward each of the jams.

Wilson and his colleagues then assessed the extent to which the attitudes expressed by the students correlated with taste ratings of the five jams as indicated by experts at Consumer Reports . They found that the attitudes expressed by the students correlated significantly higher with the expert ratings for the participants who had not listed their cognitions first. Wilson and his colleagues argued that this occurred because our liking of jams is primarily affectively determined—we either like them or we don’t. And the students who simply rated the jams used their feelings to make their judgments. On the other hand, the students who were asked to list their thoughts about the jams had some extra information to use in making their judgments, but it was information that was not actually useful. Therefore, when these students used their thoughts about the jam to make the judgments, their judgments were less valid.

MacDonald, Zanna, and Fong (1996) showed male college students a video of two other college students, Mike and Rebecca, who were out on a date. According to random assignment to conditions, half of the men were shown the video while sober and the other half viewed the video after they had had several alcoholic drinks. In the video, Mike and Rebecca go to the campus bar and drink and dance. They then go to Rebecca’s room, where they end up kissing passionately. Mike says that he doesn’t have any condoms, but Rebecca says that she is on the pill.

At this point the film clip ends, and the male participants are asked about their likely behaviors if they had been Mike. Although all men indicated that having unprotected sex in this situation was foolish and irresponsible, the men who had been drinking alcohol were more likely to indicate that they would engage in sexual intercourse with Rebecca even without a condom. One interpretation of this study is that sexual behavior is determined by both cognitive factors (e.g., “I know that it is important to practice safe sex and so I should use a condom”) and affective factors (e.g., “Sex is enjoyable, I don’t want to wait”). When the students were intoxicated at the time the behavior was to be performed, it seems likely the affective component of the attitude was a more important determinant of behavior than was the cognitive component.

One other type of match that has an important influence on the attitude-behavior relationship concerns how we measure the attitude and behavior. Attitudes predict behavior better when the attitude is measured at a level that is similar to the behavior to be predicted. Normally, the behavior is specific, so it is better to measure the attitude at a specific level too. For instance, if we measure cognitions at a very general level (e.g., “Do you think it is important to use condoms?”; “Are you a religious person?”) we will not be as successful at predicting actual behaviors as we will be if we ask the question more specifically, at the level of behavior we are interested in predicting (e.g., “Do you think you will use a condom the next time you have sex?”; “How frequently do you expect to attend church in the next month?”). In general, more specific questions are better predictors of specific behaviors, and thus if we wish to accurately predict behaviors, we should remember to attempt to measure specific attitudes. One example of this principle is shown in Figure 4.3, “Predicting Behavior from Specific and Nonspecific Attitude Measures.” Davidson and Jaccard (1979) found that they were much better able to predict whether women actually used birth control when they assessed the attitude at a more specific level.

Figure 4.3 Predicting Behavior from Specific and Nonspecific Attitude Measures. Attitudes that are measured using more specific questions are more highly correlated with behavior than are attitudes measured using less specific questions. Data are from Davidson and Jaccard (1979).Davidson, A. R., & Jaccard, J. J. (1979). Variables that moderate the attitude-behavior relation: Results of a longitudinal survey. Journal of Personality and Social Psychology, 37(8), 1364–1376.

Attitudes also predict behavior better for some people than for others. As we saw in Chapter 3, self-monitoring refers to individual differences in the tendency to attend to social cues and to adjust one’s behavior to one’s social environment. To return to our example of Magritte, you might wonder whether she is the type of person who is likely to be persuaded by peer pressure because she is particularly concerned with being liked by others. If she is, then she’s probably more likely to want to fit in with whatever her friends are doing, and she might try a cigarette if her friends offer her one. On the other hand, if Magritte is not particularly concerned about following the social norms of her friends, then she’ll more likely be able to resist the persuasion. High self-monitors are those who tend to attempt to blend into the social situation in order to be liked; low self-monitors are those who are less likely to do so. You can see that, because they allow the social situation to influence their behaviors, the relationship between attitudes and behavior will be weaker for high self-monitors than it is for low self-monitors (Kraus, 1995).

Key Takeaways

• The term attitude refers to our relatively enduring evaluation of an attitude object.
• Our attitudes are inherited and also learned through direct and indirect experiences with the attitude objects.
• Some attitudes are more likely to be based on beliefs, some are more likely to be based on feelings, and some are more likely to be based on behaviors.
• Strong attitudes are important in the sense that we hold them with confidence, we do not change them very much, and we use them frequently to guide our actions.
• Although there is a general consistency between attitudes and behavior, the relationship is stronger in some situations than in others, for some measurements than for others, and for some people than for others.

Exercises and Critical Thinking

• Describe an example of a behavior that you engaged in that might be explained by the theory of planned behavior. Include each of the components of the theory in your analysis.
• Consider a time when you acted on your own attitudes and a time when you did not act on your own attitudes. What factors do you think determined the difference?

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• Principles of Social Psychology - 1st International Edition. Authored by : Rajiv Jhangiani, Hammond Tarry, and Charles Stangor. Provided by : BC Campus OpenEd. Located at : https://open.bccampus.ca/find-open-textbooks/?uuid=66c0cf64-c485-442c-8183-de75151f13f5&contributor=&keyword=&subject= . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Regions & Countries

Most americans favor legalizing marijuana for medical, recreational use, legalizing recreational marijuana viewed as good for local economies; mixed views of impact on drug use, community safety.

Pew Research Center conducted this study to understand the public’s views about the legalization of marijuana in the United States. For this analysis, we surveyed 5,140 adults from Jan. 16 to Jan. 21, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for the report and its methodology .

As more states pass laws legalizing marijuana for recreational use , Americans continue to favor legalization of both medical and recreational use of the drug.

An overwhelming share of U.S. adults (88%) say marijuana should be legal for medical or recreational use.

Nearly six-in-ten Americans (57%) say that marijuana should be legal for medical and recreational purposes, while roughly a third (32%) say that marijuana should be legal for medical use only.

Just 11% of Americans say that the drug should not be legal at all.

Opinions about marijuana legalization have changed little over the past five years, according to the Pew Research Center survey, conducted Jan. 16-21, 2024, among 5,14o adults.

The impact of legalizing marijuana for recreational use

While a majority of Americans continue to say marijuana should be legal , there are varying views about the impacts of recreational legalization.

About half of Americans (52%) say that legalizing the recreational use of marijuana is good for local economies; just 17% think it is bad and 29% say it has no impact.

More adults also say legalizing marijuana for recreational use makes the criminal justice system more fair (42%) than less fair (18%); 38% say it has no impact.

However, Americans have mixed views on the impact of legalizing marijuana for recreational use on:

• Use of other drugs: About as many say it increases (29%) as say it decreases (27%) the use of other drugs, like heroin, fentanyl and cocaine (42% say it has no impact).
• Community safety: More Americans say legalizing recreational marijuana makes communities less safe (34%) than say it makes them safer (21%); 44% say it has no impact.

Partisan differences on impact of recreational use of marijuana

There are deep partisan divisions regarding the impact of marijuana legalization for recreational use.

Majorities of Democrats and Democratic-leaning independents say legalizing recreational marijuana is good for local economies (64% say this) and makes the criminal justice system fairer (58%).

Fewer Republicans and Republican leaners say legalization for recreational use has a positive effect on local economies (41%) and the criminal justice system (27%).

Republicans are more likely than Democrats to cite downsides from legalizing recreational marijuana:

• 42% of Republicans say it increases the use of other drugs, like heroin, fentanyl and cocaine, compared with just 17% of Democrats.
• 48% of Republicans say it makes communities less safe, more than double the share of Democrats (21%) who say this.

Demographic, partisan differences in views of marijuana legalization

Sizable age and partisan differences persist on the issue of marijuana legalization though small shares of adults across demographic groups are completely opposed to it.

Older adults are far less likely than younger adults to favor marijuana legalization.

This is particularly the case among adults ages 75 and older: 31% say marijuana should be legal for both medical and recreational use.

By comparison, half of adults between the ages of 65 and 74 say marijuana should be legal for medical and recreational use, and larger shares in younger age groups say the same.

Republicans continue to be less supportive than Democrats of legalizing marijuana for both legal and recreational use: 42% of Republicans favor legalizing marijuana for both purposes, compared with 72% of Democrats.

There continue to be ideological differences within each party:

• 34% of conservative Republicans say marijuana should be legal for medical and recreational use, compared with a 57% majority of moderate and liberal Republicans.
• 62% of conservative and moderate Democrats say marijuana should be legal for medical and recreational use, while an overwhelming majority of liberal Democrats (84%) say this.

Views of marijuana legalization vary by age within both parties

Along with differences by party and age, there are also age differences within each party on the issue.

A 57% majority of Republicans ages 18 to 29 favor making marijuana legal for medical and recreational use, compared with 52% among those ages 30 to 49 and much smaller shares of older Republicans.

Still, wide majorities of Republicans in all age groups favor legalizing marijuana at least for medical use. Among those ages 65 and older, just 20% say marijuana should not be legal even for medical purposes.

While majorities of Democrats across all age groups support legalizing marijuana for medical and recreational use, older Democrats are less likely to say this.

About half of Democrats ages 75 and older (53%) say marijuana should be legal for both purposes, but much larger shares of younger Democrats say the same (including 81% of Democrats ages 18 to 29). Still, only 7% of Democrats ages 65 and older think marijuana should not be legalized even for medical use, similar to the share of all other Democrats who say this.

Views of the effects of legalizing recreational marijuana among racial and ethnic groups

Substantial shares of Americans across racial and ethnic groups say when marijuana is legal for recreational use, it has a more positive than negative impact on the economy and criminal justice system.

About half of White (52%), Black (53%) and Hispanic (51%) adults say legalizing recreational marijuana is good for local economies. A slightly smaller share of Asian adults (46%) say the same.

Criminal justice

Across racial and ethnic groups, about four-in-ten say that recreational marijuana being legal makes the criminal justice system fairer, with smaller shares saying it would make it less fair.

However, there are wider racial differences on questions regarding the impact of recreational marijuana on the use of other drugs and the safety of communities.

Use of other drugs

Nearly half of Black adults (48%) say recreational marijuana legalization doesn’t have an effect on the use of drugs like heroin, fentanyl and cocaine. Another 32% in this group say it decreases the use of these drugs and 18% say it increases their use.

In contrast, Hispanic adults are slightly more likely to say legal marijuana increases the use of these other drugs (39%) than to say it decreases this use (30%); 29% say it has no impact.

Among White adults, the balance of opinion is mixed: 28% say marijuana legalization increases the use of other drugs and 25% say it decreases their use (45% say it has no impact). Views among Asian adults are also mixed, though a smaller share (31%) say legalization has no impact on the use of other drugs.

Community safety

Hispanic and Asian adults also are more likely to say marijuana’s legalization makes communities less safe: 41% of Hispanic adults and 46% of Asian adults say this, compared with 34% of White adults and 24% of Black adults.

Wide age gap on views of impact of legalizing recreational marijuana

Young Americans view the legalization of marijuana for recreational use in more positive terms compared with their older counterparts.

Clear majorities of adults under 30 say it is good for local economies (71%) and that it makes the criminal justice system fairer (59%).

By comparison, a third of Americans ages 65 and older say legalizing the recreational use of marijuana is good for local economies; about as many (32%) say it makes the criminal justice system more fair.

There also are sizable differences in opinion by age about how legalizing recreational marijuana affects the use of other drugs and the safety of communities.

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Table of contents, most americans now live in a legal marijuana state – and most have at least one dispensary in their county, 7 facts about americans and marijuana, americans overwhelmingly say marijuana should be legal for medical or recreational use, clear majorities of black americans favor marijuana legalization, easing of criminal penalties, religious americans are less likely to endorse legal marijuana for recreational use, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .