The Psychology of Attitudes, Motivation, and Persuasion. In Albarracin & Johnson (Eds.), Handbook of Attitudes.

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The Psychology of Attitudes and Persuasion
The Psychology of Attitudes and Persuasion
Dolores Albarracín, Aashna Sunderrajan, Sophie Lohman, Sally Chan, & Duo Jiang
University of Illinois at Urbana Champaign
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The Psychology of Attitudes and Persuasion
The Psychology of Attitudes and Persuasion
A quick look at the front page of the New York Times shows the following headlines:
12 Oscar Nominations for The Revenant
Syrians Tell a Life Where Famine is a Weapon
Cruz Did Not Report Goldman Sacks Loan in Senate Race
What to Expect of G.O.P. Deba: Escalating Attacks
Terrorists Attacks Kill at Least Two in Jakarta, Police Say
Each and every headline connects with attitudes, as evaluations that drive our actions, and in
some of these cases, our inactions. Attitudes are not only part of the news consumed worldwide,
but they are a subject of general interest that has increased over time. For example, the number
of Google searches for the term attitude shows an increasing trajectory displayed in Figure 1. As
another example, Amazon lists over 30,000 books containing the word attitude in the title, which
clearly displays the popular interest in understanding and also changing attitudes.
The topic of attitudes is particularly pertinent for this handbook, because the psychology
of attitudes is generally a social psychology of attitudes. Clearly, cognitive psychology has
contributed to our understanding of the microprocesses involved in attitude formation and
change, and biological psychology can account for the sensorial mechanisms underlying
preferences for certain objects, such as foods. There is, however, a reason why attitudes have
been a focus in social psychology: Attitudes are often learned from others, make individuals
similar to members of their groups, and are affected by social pressure and persuasion – the act
of attempting to change the attitudes of another person. This chapter discusses these critical
issues, addressing classic and contemporary questions. As shown in Figure 2, we consider
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attitudes in relations to beliefs, intentions, behaviors and goals, and also discuss the influence of
various processes of attitude formation and change, including persuasive communications.
Attitudes
A definition of an attitude needs to be one that is sufficiently comprehensive to cover the
extent of current literature, and generalizable to remain useful with evolving research trends
(Eagly & Chaiken, 2007; Gawronski, 2007). What has been consistent in the multiple
conceptualizations of the attitude construct is that evaluation is the key component (Ajzen, 2001;
Albarracin, Zanna, Johnson & Kumkale, 2005; Eagly & Chaiken, 1993; Gawronski, 2007; Maio
& Haddock, 2009). Thus, in this chapter we define attitude as evaluation.
The target or subject matter of an attitude can be an object, a person, or an abstract idea.
Attitudes towards objects span many applications of social psychology to such domains as
marketing (e.g., attitudes towards products), advertising (e.g., attitudes towards ads), political
behavior (e.g., attitudes towards political candidates, parties, or voting), and health (e.g.,
attitudes towards protective behaviors, new medications, or the health system). Attitudes towards
other people are often investigated under the umbrella of interpersonal liking. Attitudes towards
abstract ideas involve values, such as judging freedom or equality as desirable.
Attitudes also vary in terms of specificity vs. generality. An attitude towards Donald
Trump is specific in target (e.g., his hairdo comes to mind), but many attitudes are general. For
example, some individuals hold relatively positive attitudes towards all objects, whereas others
dislike most people, objects, and ideas (Hepler & Albarracin, 2013). Further, attitudes concerning
an object can have different degrees of specificity with respect to temporal and spatial contexts
(see Ajzen & Fishbein, 2005; Fishbein & Ajzen, 1975). For example, receiving the flu vaccine in
the next month represents less commitment than consistently receiving the flu vaccine every fall.
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Likewise, receiving the flu vaccine in Chicago may seem more desirable than receiving the flu
vaccine while vacationing in the South Pacific.
Measurement also has implications for distinctions among attitudes. The development of
attitude measurement techniques, for instance, has enabled researchers to measure attitudes
indirectly rather than relying exclusively on explicit ratings of liking or approval (Bassili &
Brown, 2005; Gawronski, 2007). These indirect measures of attitudes, referred to as implicit, are
intended to assess automatic evaluations that are generally difficult to gauge using explicit self-
reports. For example, the effectiveness of implicit measures is implied by evidence showing that
they are often inconsistent with (Petty, Fazio & Briñol, 2009), and predict different outcomes
from (Maio & Haddock, 2009), self-reported or explicit attitudes.
The divergence between implicit and explicit attitudes has commonly been seen as
evidence suggesting that they measure two distinct representations of attitudes, namely
unconscious and conscious processes (Wilson, Lindsey & Schooler, 2000). Alternatively, the lack
of intercorrelation between implicit and explicit attitudes has been seen to stem from the fact that
each measure captures upstream and downstream processes, specifically automatic responses and
intentionally edited judgments related to the same attitude (Fazio, 1995; Nier, 2005). Some
scholars have even questioned whether attitudes can be regarded as stable entities, or if they are
instead constructed only when the attitude object is encountered (e.g., Schwarz, 2007). In an
attempt to address this debate, Hofmann, Gawronski, Gschwendner, Le and Schmitt (2005)
conducted a meta-analysis of 126 studies examining the relation between implicit and explicit
representations. In this synthesis, the correlation between the Implicit Association Test (IAT) and
explicit attitude measures was r = .24, but varied as a function of psychological and
methodological factors (Hofmann et al., 2005). For instance, the correlation between implicit and
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explicit measures varied as a function of the amount of cognitive effort used during explicit self-
report tasks, suggesting different transformations of a single evaluative response.
Neuroimaging studies have observed similar differences between implicit and explicit
attitudes. For example, the structures involved during automatic evaluation have been found to
include the amygdala, the insula and the orbitofrontal cortex (Cunningham, Johnson, Gatenby,
Gore & Banaji, 2003; Cunningham, Packer, Kesek & van Bavel, 2009; Cunningham, Raye &
Johnson, 2004; Wright et al., 2008). In contrast, those involved during controlled evaluations
include regions of the anterior cingulate cortex, including the dorsal anterior cingulate cortex
(Cunningham et al., 2003; Cunningham et al., 2004; Critchley, 2005). Together, these studies
also suggest a neural distinction between the processes engaged during automatic and deliberate
processing, and is compatible with the notion that implicit measures capture earlier, spontaneous,
affective processes, whereas explicit attitudes reflect more deliberate adjustments on the basis of
current goals or social desirability concerns.
Behavior, Beliefs, Intentions, and Goals
Behavior is typically defined as the overt acts of an individual (Albarracin et al., 2005)
and is generally assumed to partly stem from attitudes. One of the earliest studies looking at the
association between attitudes and behavior was conducted by LaPiere (1934). LaPiere travelled
across the United States with a young Chinese couple and found that, despite widespread anti-
Asian prejudice, only one establishment refused service to him and his companions. Several
months later, LaPiere sent out a letter to the establishments he visited and asked whether they
would accommodate Asians as guests. As only one respondent indicated that they would, the
study was presented as a demonstration of a weak relation between attitudes (the racial
prejudice) and behaviors.
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LaPiere's study, however, suffered from important methodological flaws. One was that
LaPiere was unable to ensure that the individual who served him and his companions was the
same individual who responded to the letters. In fact, considerable research on the attitude-
behavior relation now indicates that attitudes are fairly good predicts of behaviors. For example,
a meta-analytic review of the literature found that the average correlation between attitudes and
behavior is r = .52 (Glasman & Albarracίn, 2006), and that this association varies with a number
of established moderators (see further details later in this chapter).
Two additional concepts are central to the psychology of attitudes and persuasion:
Intentions and beliefs. An intention is a willingness to perform a behavior. Intentions often
emerge from broader goals – desirable endstates – that can be achieved via multiple, sustained
behaviors, are not fully controllable results, and require external help or resources (Ajzen &
Fishbein, 2005). For example, people develop intentions to increase physical activity with the
goal of losing weight, but executing the intended behavior is no guarantee of success.
Like attitudes, goals can be specific or general. On the one hand, attitude-behavior
researchers have generally studied fairly specific goals, such as the goal to quit smoking (see
Ajzen & Fishbein, 1980). When set, these goals are facilitated by intentions to perform specific
actions, like throwing away smoking related paraphernalia or avoiding friends who smoke. The
intention to quit smoking or achieve a similar goal is an excellent predictor of actual behavior.
For example, meta-analyses of specific health behaviors, such as condom use and exercise, have
yielded average intention-behavior correlations ranging from .44 to .56 (Albarracín, Johnson,
Fishbein, & Muellerleile, 2001; Godin & Kok, 1996; Hausenblas, Carron & Mack, 1997;
Sheeran & Orbell, 1998). On the other hand, traditional goal researchers have studied more
general goals such as the achievement motivation or the affiliation need (Elliot & Church, 1997;
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Maslow, 1970). These goals have a weak correspondence to specific behaviors, probably
because they are carried out over long periods of time and across many domains. Achievement or
affiliation motivations correspond to personality or stable patterns of behavior (for a recent
review, see Moskowitz, Li, & Kirk, 2004), and can either be measured or manipulated with
methods borrowed from cognitive psychology (e.g., presenting semantically linked words; see
Hart & Albarracin, 2009; Weingarten et al., 2015). Perhaps the most general class of all
investigated goals (see Albarracin et al., 2008; Albarracin, Hepler, Tannenbaum, 2011) entails
general action goals, which are generalized goals to engage in action (e.g., activated with
instructions such as go), as well as general inaction goals, which are generalized goals to not
engage in action (e.g., activated with instructions such as rest). These goals are diffuse desired
ends that can mobilize the execution of more specific activities. Hence, their activation may
trigger the pursuit or interruption of any particular (overt or covert) behavior that is subjectively
relevant to the goal. Action goals imply a need to do irrespective of what one does; inaction goals
imply a need to abstain from doing, irrespective of the abstinence domain.
A belief can be defined as a person’s subjective probability of a relation between the
object of the belief and some other object, value, concept, or attribute, and affects people’s
understanding of themselves and their environments (Fishbein & Ajzen, 1975). A
conceptualization proposed by McGuire (1960, 1981) and extended by Wyer and Goldberg
(1970; see also Wyer, 1974) addressed how prior beliefs can influence new beliefs and attitudes.
McGuire (1960) stated that two cognitions, A (antecedent) and C (conclusion), can relate to each
other by means of a syllogism of the form A; if A, then C; C. This structure implies that the
probability of C (e.g., an event is good) is a function of the beliefs in the premise or antecedent,
and beliefs that if A is true and if A is true, C is true. Further, Wyer (1970; Wyer & Goldberg,
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1970) argued that C might be true for reasons other than those included in these premises. That
is, beliefs in these alternate reasons should also influence the probability of the conclusion (not
A; if not A, then C). Hence, P(C) should be a function of the beliefs in these two mutually
exclusive sets of premises, or:
P(C) = P(A)P(C/A) + P(~A)P(C/~A), [1]
where P(A) and P(~A) [= 1-P(A)] are beliefs that A is and is not true, respectively, and P(C/A)
and P(C/~A) are conditional beliefs that C is true if A is and is not true, respectively.
A limitation of the conditional inference model described above is the use of a single
premise. Although other criteria are considered, these criteria are lumped together in the value of
P(C/~A), or the belief that the conclusion is true for reasons other than A. In contrast, other
formulations consider multiple factors. Slovic and Lichtenstein (1971), for example, postulated
that people who predict an unknown event from a set of cues are likely to combine these cues in
an additive fashion. Therefore, regression procedures can be used to predict beliefs on the basis
of the implications of several different pieces of information. In this case, the regression weights
assigned to each piece provide an indication of its relative importance.
Multiple-regression approaches can be useful in identifying individual differences in the
weights given to different types of cues (Wiggins, Hoffman, & Taber, 1969). Nevertheless, the
assumptions that underlie these approaches are often incorrect (Anderson, 1971, 1981; Fishbein
& Ajzen, 1975; Wiggins & Hoffman, 1968). Birnbaum and Stegner (1979), for example, found
that participants’ estimates of a car’s value was an average of its blue book value and the opinion
of another person, with the weight of each piece of information depending on the credibility of
its source.
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In many instances, however, neither summative nor averaging belief models may be
applicable. Kahneman and Tversky (1983) provide strong evidence that people’s estimates of the
conjunction of two features (e.g., the likelihood that a woman is a feminist bank-teller) are not
predictable from their estimates of each feature considered in isolation (i.e., being a feminist or
being a bank teller). In these instances, people appear to process the information configurally
rather than construing the implications of each piece of information separately. The conditions in
which different combinatorial processes underlie the beliefs that people report (as well as other
judgments they make) require more detailed analyses than can be provided in this chapter (for a
general discussion of these matters, see Wyer & Carlston, 1979).
Further Characterizing Attitudes
Attitude Structure
The structure of an attitude can be explicated using models of affective feelings. A
popular understanding of the structure of affect and emotions incorporates the dimensions of
positive vs. negative valence and high vs. low arousal (Bradley, Codispoti, Cuthbert, & Lang,
2001; Russell, 2003; Smith & Ellsworth, 1985; for reviews, see Albarracin & Vargas, 2010;
Clore & Schnall, 2005; Schimmack & Crites, 2005). People feel sad, angry, content, or excited,
and each state varies not only in negative or positive valence but also in associated arousal
(Russell, 2003). Arousal has proved to be difficult to describe, but generally entails autonomic
activation measurable by changes in skin conductance, heart rate, or brain waves (see Bradley &
Lang, 2007; Cacioppo, Berntson, & Crites, 1996). Feeling anxious, tense, alert, and excited have
high autonomic activation or arousal in common (e.g., high heart and breathing rate), whereas
feeling sad and content have low autonomic activation or arousal in common (e.g., lower heart
and breathing rate; somnolence).
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As evaluations are valenced responses (positive vs. negative), attitudes can also be
mapped onto a model with valence and arousal as distinct dimensions (Albarracin & Vargas,
2010). This model appears in Figure 3. On the valence axis, individuals may dislike or like a
political candidate, and may dislike or like a particular taste. Furthermore, attitudes can be
mapped onto the arousal dimension because they vary in extremity, importance, confidence, or
the degree to which they elicit strong emotional responses, such as excitement (see Cuthbert,
Schupp, Bradley, Birbaumer, & Lang, 2000; Fabrigar, MacDonald, & Wegener, 2005; Lang,
Öhman, & Vaitl, 1988). For example, highly involving objects such as abortion, gay marriage,
and marijuana legalization often trigger strong attitudes that are infused with feelings, and
connect to other important attitudes, such as personal values and self-esteem (the evaluations of
oneself as a person; Eagly & Chaiken, 1993; Fabrigar et al., 2005). These attitudes are often
reported as extreme in responses to attitude scales (Judd & Brauer, 1995), are held with high
confidence (Abelson, 1988), are easy to recall (Judd & Brauer, 1995), and are passionately
defended against external attacks (Johnson, Maio & Smith-McLallen, 2005; Petty, Tormala, &
Rucker, 2004). Some of this evidence is reviewed in upcoming sections in this chapter.
Adhering to a valence/arousal model does not require adherence to a circumplex (see also
Remington, Fabrigar, & Visser, 2000). For instance, objects with either extremely positive or
extremely negative valence are often important and generate high autonomic arousal (for a
review, see Bradley et al., 2001). In this case, high positive and negative valences go along with
high arousal, whereas neutral valences go along with low arousal. Mapping objects onto
independent valence and arousal dimensions would thus produce a U-type of pattern, rather than
equally populated quadrants (see Bradley et al., 2001; Remington et al., 2000).
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Neuropsychological research indicates that specific structures in the brain respond to
stimuli in ways that suggest an intricate relation between valence and arousal. As one case in
point, the amygdala, an almond-shaped group of neurons located deep in the medial temporal
lobes of the brain, plays a critical role in evaluation (Hamann, Ely, Hoffman, & Kilts, 2002;
Irwin et al., 1996). The amygdala is active during affective judgments of emotional pictures,
words, and odors (Cunningham et al., 2004; Phan et al., 2004; Royet, Plailly, Delon-Martin,
Kareken, & Segebarth, 2003), when there is negative (vs. positive) stimuli (Cunningham et al.,
2003; Morris et al., 1996; Reekum et al., 2007), and when the stimuli have emotional meaning
(Bechara, Damasio, Damasio, & Lee, 1999). Also, the amygdala connects with the insula and the
anterior cingulate cortex, and these three structures all respond to both valence and arousal
(Cunningham et al., 2004; Cunningham & Zelazo, 2007; Rempel-Clower, 2007; Wright et al.,
2008). The visual cortex also appears sensitive to both valence and arousal, as judged from an
fMRI study revealing greater activation in response to pleasant and unpleasant images, compared
to neutral images (Lang et al., 1998).
Attitude Bases
Attitudes are based on affective, cognitive, and behavioral information (Albarracin et al.,
2005; Eagly & Chaiken, 1993). The affective component consists of feelings and emotions
related to an attitude object, the cognitive component is composed of beliefs, thoughts and
attributes associated with an attitude object, and the behavioral component comprises of past
behaviors or experiences with an attitude object (Eagly & Chaiken, 1993; Fabrigar et al., 2005).
Although earlier attitude research conceptualizes cognitive, affective and behavioral
components as facets of attitudes, there is now consensus that these are the bases for attitudes
without being attitudes themselves. Breckler (1984), for example, had participants report their
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cognitive, affective and behavioral responses about snakes. Using the content of participants'
responses, Breckler then computed a score for each of the components and found that they were
empirically distinct. Equally important, however, cognitive, affective and behavioral measures
separately predict attitudes. Haddock, Zanna and Esses (1994) looked at the attitude of Canadian
students towards Native Canadians. Using open-ended measures of cognition, affect and
behavior, the researchers found that participants' past experience with Native Canadians
predicted their attitudes, independently of cognition and affect.
Attitude Functions
Attitudes can serve different motives and functions for individuals. The earliest, and most
prominent, functional theories proposed were by Smith, Bruner and White (1956) and Katz
(1960). Although these theories differ in the labels they use, they both see attitudes as constructs
designed to serve an individual’s social and emotional needs (Shavitt & Nelson, 2002; for a
review, see Kruglanski & Stroebe, 2005). Katz (1960), for example, proposed that attitudes serve
four functions. The knowledge function represents attitudes that organize information about the
environment. The utilitarian function reflects attitudes that summarize the rewards and
punishments associated with an attitude object. The ego-defensive function embodies attitudes
that protect the self. Finally, the value-expressive function represents attitudes that express an
individual’s self-concept. Smith et al. (1956) additionally proposed a social-adjustment
function, which reflects attitudes that help to identify with those we like and de-identify with
those we dislike.
In recent years, research on the function of attitudes has focused on the more specific
functions that attitudes serve (Maio & Haddock, 2009). In particular, functional theories have
seen a resurgence in persuasion literature, where they have been important in emphasizing the
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necessity of understanding the motivational basis of the attitudes we strive to change (Shavitt &
Nelson, 2002). Research in advertising has found that the use of functionally matched ads elicits
more favorable attitudes and purchase intentions, compared to functionally mismatched ads
(Shavitt, 1990), and that this effect persists when individuals are asked to write their own ads
(Shavitt, Lowrey & Han, 1992). Similarly, research in the domain of health behavior has found
that attitude functions can serve as determinants of behavioral intentions with respect to regularly
participating in physical activity (Wang, 2009) and register as an organ donor (Wang, 2012).
The literature in the functional area is, however, still lacking (Maio & Olson, 2000;
Shavitt & Nelson, 2002). One reason is that individuals are often poor at identifying the
functional reasons behind their attitudes (Maio & Haddock, 2009; Nisbett & Wilson, 1977). A
second is that attitude functions are not always distinct, making the categorization of attitudes
into separate functional types difficult (Maio & Haddock, 2009). Nonetheless, research in this
area is sure to thrive in the next decade, as functional theories are necessary to shed light on the
motivational basis behind attitudes and behavior.
Attitude Dimensions
Attitude strength is the degree to which an attitude persists over time, is resistant to
change, and influences cognitions and behavior (Fazio, 1995; Krosnick & Smith, 1994).
Therefore, the strength with which an attitude is held informs us of when and which type of
attitudes are predictive of behavior. In the literature, attitude strength is seen as a combination of
multiple dimensions (including extremity, intensity, certainty, importance, interest, knowledge,
accessibility and affective-cognitive consistencies), which interact to influence the
consequentiality of attitudes. Initially, as these dimensions predicted most of the features of
strong attitudes, they were seen as forming a single construct, namely attitude strength (Miller &
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Peterson, 2004). Exploratory factor analyses provided evidence for this assertion, by determining
that the different dimensions of attitude strength could be grouped under a few factors (Bassili,
1996). However, due to problems with measurement error, some argued that these conclusions
were exaggerated (Krosnick, Boninger, Chuang, Berent & Carnot, 1993). Krosnick et al. (1993),
in fact, found that only a few pairs of dimensions were strongly correlated with each other. This
leads to the position that attitude strength is not a unitary construct, but rather, consists of
multiple dimensions (Krosnick et al., 1993; Miller & Peterson, 2004; Visser, Krosnick &
Simmons, 2003).
As space constraints prevent us from describing every dimension associated with attitude
strength, we focus on a few properties and some associated findings: extremity, importance,
certainty, and accessibility (for reviews, see Bassili, 2008; Krosnick et al., 1993; Krosnick &
Smith, 1994; Miller & Peterson, 2004). Attitudes vary in direction, ranging from positive to
negative. Attitude extremity is thus the degree to which an individual’s attitude deviates from
neutrality in either a positive or negative direction (Bassili, 2008; Krosnick et al., 1993; Krosnick
& Smith, 1994). A polarization in attitude can be caused by several processes, including
activation of the attitude via thoughts (Tesser, Martin & Mendolia, 1995), and attitude repetition
(Judd & Brauer, 1995). Insults have also been shown to increase attitude extremity (Abelson &
Miller, 1967), with more recent work suggesting that arguments associated with the self trigger
an ego-defensive mechanism that can lead to attitude polarization (De Dreu & van Knippenberg,
2005).
Attitude importance is characterized by the degree to which an individual is personally
invested in an attitude (Bassili, 2008; Krosnick et al., 1993; Krosnick & Smith, 1994) and
manifests itself in self-reported caring about the attitude object. The more important an attitude
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is, the more it is resistant to change, and stable over time (Krosnick & Smith, 1994). Attitudes
that are important also lead to selective exposure and elaboration of attitude-relevant information
(Holbrook, Berent, Krosnick, Visser & Boninger, 2005).
Attitude certainty is the degree to which an individual is confident in the attitude they
hold (Krosnick et al., 1993; Krosnick & Smith, 1994). Unlike many of the other dimensions,
attitude certainty is a metacognitive attribute, as it attaches an explanation of certainty onto a
primary cognition (Petty, Briñol, Tormala, & Wegener, 2007). Certainty in one's attitude is
determined by such factors as direct experience (Wu & Shaffer, 1987) and repetition (Holland,
Verplanken & van Knippenberg, 2003) and confers resistance to persuasive messages (Krosnick
& Smith, 1994).
Research suggests that a unifying framework of attitude strength is attitude accessibility
(Ajzen, 2012). Accessibility refers to the strength of the association between an attitude object
and its evaluation and is observed as the ease with which an attitude is activated from memory at
the appearance of the attitude object (Fazio, Sanbonmatsu, Powell & Kardes, 1986).
Accessibility is partly determined by the frequency with which an attitude is activated, as well as
the perceived diagnosticity of the information regarding an attitude, which includes the
emotional reaction elicited by the attitude object, prior experience with and behavior towards the
attitude object, and the foundation upon which the information is obtained (e.g., if it’s based on
sensory information, or direct experience; Fazio, 1995).
In addition to studies looking at the dimensions of attitude strength in isolation, there
have been studies that have tried to explore attitude strength within a social context, including
the heterogeneity of an individual’s social network (Levitan & Visser, 2009; Visser & Mirabile,
2004). For example, Visser and Mirabile (2004) found that the composition of an individual's
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social network can affect the strength of their attitudes, with those embedded in more
heterogeneous networks being more susceptible to persuasive messages. This finding highlights
the social nature of attitudes, and thus the importance of examining the influence of different
dimensions on attitude strength in a social context.
The Origins of Attitudes
Many attitudes are formed early in life. Others are formed as we encounter new products,
new places, and new people throughout our lives, and yet others change as a result of experience,
new information, or social influence. Much of the research conducted in social psychology relies
on introducing information about novel topics, and, as such, models attitude formation more than
change. In the next sections, we review findings that concern attitude formation and change
separately, even though some models of formation have implications for change.
Processes Underlying Attitude Formation
Direct Experience
Having direct experience with an attitude object is perhaps the most basic, and obvious,
way to form attitudes. Direct experience influences the likelihood that an attitude will be
sufficiently arousing as to be activated when the object is encountered again. In one study, some
participants were allowed to play with examples of five types of puzzles (direct experience)
whereas other participants were presented with examples of the types of puzzles already solved
by another person (indirect rather than direct experience by the participants themselves). All
participants later reported how interesting they found each type of puzzle. Following that, they
were all allowed to play with the puzzles for up to 15 minutes. The findings from this study
indicated that attitudes (the interest ratings) were stronger predictors of actual behavior in the
direct than indirect experience condition (Regan & Fazio, 1977).
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Fazio and his colleagues (e.g., Fazio, Powel, & Herr, 1983) have proposed that direct
experience produces high attitude-behavior correlations because attitudes formed from direct
experience are easier to retrieve from permanent memory. In this sense, direct experience appears
to be analogous to repeatedly expressing or reporting attitudes. A meta-analysis of the behavioral
impact of recently formed attitudes (Glasman & Albarracín, 2006; see also Kraus, 1995)
confirmed this possibility. That is, direct experience and repeated expression/report of the
attitude correlated with faster reporting of attitudes (for direct experience, r = .60; for repeated
expression, r = .24; Glasman & Albarracín, 2006). Further, the influence of direct experience and
attitude report on the attitude-behavior relation was mediated by response speed (from direct
experience to response speed, r = .43, from response speed to the attitude-behavior correlation, r
= .82).
However, direct experience does not seem to improve attitude-behavior correspondence
when the bases for attitudes and the bases for behavior are different. For example, an attitude
based on experienced feelings, such as how much pleasure is associated with a video game (an
affective experience), does not predict the use (or lack thereof) of a video game for career
advancement or learning purposes (an instrumental behavior; Millar & Tesser, 1986). Indeed, the
attitude behavior correlation is r = .59 when the experience with the attitude is similar to the
experience associated with the predicted behavior (i.e., both instrumental or both affective) but r
= .39 when the two are dissimilar; Glasman & Albarracín, 2006). Second, as Hoch and Ha (1986,
see also Ha & Hoch, 1989; Wooten & Reed, 1998) and Albarracín and McNatt (2005)
demonstrated, acquiring direct experience with the object is as important in guiding behavior as
receiving information that is unambiguous and consistent (i.e., evaluative diagnosticity, Reed,
Wooten, & Bolton, 2002). When all else is equal, direct experience stimulates the use of a
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resulting summary attitude as a basis for future behavior. Direct experience, however, sometimes
provides mixed evidence about the desirability of an object (e.g., people are presented with
neutrally valenced information, Reed et al., 2002, or products with standard qualities, Hoch &
Ha, 1986). In these conditions, direct experience actually decreases attitude-behavior
correspondence (Albarracín & McNatt, 2005; Glasman & Albarracín, 2006).
The role of virtual experience with an attitude object has also received research attention.
For example, one no longer has to actually get a haircut to experience the new look. Instead, one
can purchase computer programs that digitally alter one’s image in accordance with the planned
hair style. In a study of virtual direct experience (Griffith & Chen, 2004), some products, such as
movies and music, were easy to experience in a digital fashion. Participants were simply
presented with ads about these products and did or did not view the product. Other products,
such as clothes and apparel, are more difficult to experience virtually. Still, the researchers
created movie clips of the actual experience of, for example, viewing objects with a fictitious
pair of sunglasses. Thus, for these products as well, some conditions allowed for viewing (virtual
experience present) whereas others did not (virtual experience absent). The results from this
study indicated that when the experience was easy to digitalize, virtually experiencing and not
experiencing the product had different effects. Participants had more positive evaluations and
stronger intentions to buy the product following the virtual viewing than in the absence of virtual
viewing. However, these effects disappeared when the products were difficult to digitalize, in
which case attitudes and purchasing intentions were the same with and without the virtual
experience.
Mere Exposure
Mere exposure is a phenomenon in which an attitude towards a stimulus becomes more
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favorable with increasing frequency of exposure to the stimulus (Zajonc 1968). In the classic
example of the mere exposure paradigm, English-native participants were shown unknown
Chinese characters from zero to twenty five times. Participants liked the characters better when
they were exposed to them more frequently (Zajonc, 1968). In another typical study, participants
subliminally presented with 10 different polygons, were later asked to indicate which one of two
polygons they had been shown earlier, and which one they preferred. Recognition of the
polygons was just below chance (48%), but preference for old polygons was substantially higher
than chance (60%; Kunst-Wilson & Zajonc, 1980).
A sizable amount of research has been carried out to examine the mere exposure effect
over the years. A meta-analysis summarizing 208 empirical studies from 134 articles published
between 1968 and 1987 obtained a moderate effect size of 0.26 (Bornstein, 1989). The effect is
true for a variety of stimuli, including sounds (r = .24), ideographs (r = .22), nonsense
words/syllables (r = 24), photographs (r = .37), meaningful words/names (r = .49), polygons (r =
.41), real people/objects (r = .20) – except for abstract paintings, drawings, and matrices (r =
−.03). Moreover, the effect tends to be stronger when a heterogeneous vs. homogenous pool of
stimuli are presented (r = .30 vs. r = -.02, respectively), when exposure times are shorter (e.g.,
less than one second, r = .41), when the maximum number of stimulus presentations is up to nine
(r = .21), when there is some delay between stimulus exposure and evaluation (r = .22), and
when the participants are adults instead of children aged 12 or under (r = .30 vs. r = -.05,
respectively).
Various accounts have been proposed to explain the mechanism underlying the mere
exposure effect (e.g. Demand effects; Berlyne, 1970; Grush, 1976; Stang, 1974). Among these
explanations, the perceptual fluency/misattribution account – the process by which the ease of
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perceiving and processing previously encountered stimuli (Jacoby, Kelley, Brown, & Jasechko,
1989) is misattributed as liking (Bornstein & D’Agostino, 1994; Smith, 1998) – emerged as a
more parsimonious, and thus, the most likely candidate. If increased liking toward previously
shown stimuli is due to misattributed perceptual fluency, individuals should discount or even
over-discount the influence of irrelevant fluency when they learn the true source of the fluency.
For example, participants have been shown to make negative adjustments in evaluating
previously seen stimuli when they are led to believe that they have seen it before (Bornstein &
D’Agostino, 1994). Additionally, the mere exposure effect is less likely to happen when fluency
is disconnected from, whereby one can hardly misattribute it to the evaluative judgment.
Recent work, however, has cast some doubt on the perceptual fluency/misattribution
account. Evidence has been accumulated that people prefer stimuli that they recognize (Anand &
Sternthal, 1991; Brooks & Watkins, 1989; Fang, Singh, & Ahluwalia, 2007; Newell & Shanks,
2007; Szpunar, Schellenberg, & Pliner, 2004; Whittlesea & Price, 2001; cf. Weisbuch, Mackie, &
Garcia-Marques, 2003), suggesting that the mere exposure process requires at least some
intentional cognitive processing. Furthermore, evidence also suggests that the effect happens for
novel stimuli, and has positive effects on mood (Monahan, Murphy, & Zajonc, 2000) – neither of
which can be explained by the perceptual fluency/misattribution hypothesis, but can be explained
by fluency more generally.
The role of fluency in evaluative judgments has also been proposed to be potentially
independent of intentional information processing (i.e., the Hedonic Fluency Model; Winkielman
& Cacioppo, 2001; Winkielman, Schwarz, Fazendiero, & Reber, 2003). According to the hedonic
fluency model, perceptual and/or conceptual fluency may or may not be consciously recognized.
The fluency associated with fast and effortless mental processing leads to positive affect
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detectable with even physiological measures (Harmon-Jones & Allen, 2001; Winkielman &
Cacioppo, 2001). This positive affect may be transferred to evaluative judgments of previously
seen stimuli, as well as novel stimuli, self-reported affect (Monahan et al., 2000) and
physiologically-measured mood (Harmon-Jones & Allen, 2001; Winkielman & Cacioppo, 2001).
A meta-analysis of 90 studies examining the effect of perceptual fluency on affective judgments
obtained a medium effect size (Cohen’s d = .50; Warth, 2008). The effect was moderated by
whether participants were aware of the experimental manipulation, such that participants who
were aware tended to discount fluency and use other inputs to form attitudes, and neutral and
positive stimuli produced larger fluency effects than negative stimuli.
More recently, an embodied account has also been proposed to explain mere exposure
(Topolinski & Strack, 2009, 2010), arguing that the fluency responsible for increased liking of
repeated stimuli comes from specific motor responses associated with such stimuli. The idea is
that specific sensory organs (e.g., eyes, mouths, and hands) register and remember the fluency
when people process stimuli, and this embodied fluency is later activated to make judgments
about the stimuli. Therefore, preventing people from registering the embodied fluency when they
are presented with the stimuli, and from retrieving such fluency when they make judgments
about the stimuli, may decrease the mere exposure effect. For example, asking participants to
perform a secondary oral motor task when they are repeatedly presented names of actors
decreases the mere exposure effect for those names (Topolinski and Strack, 2010), and chewing
gum while evaluating stimuli eliminates mere exposure effects for words but not for visual
characters (Topolinski & Strack, 2009).
Evaluative Conditioning
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Evaluative conditioning (EC), a process similar to Pavlovian (classical) conditioning
(Martin & Levey, 1978, 1994; Levey & Martin, 1975), occurs when a conditioned stimulus (CS,
such as tempting food) is consistently presented before an unconditioned stimulus (US, such as a
new logo), causing the US to take on the valence of the CS. However, EC differs from the
classical condition in several aspects. Above all, while classical conditioning requires sufficient
awareness, EC may happen with or without awareness (De Houwer, Thomas, & Baeyens, 2001).
Classical conditioning occurs when people are aware of and recognize the CS and US. If an
individual does not recognize that an electric shock always follows the ringing bell, they should
not develop a cringe response to the ringing bell. In contrast, EC may happen with awareness
(Purkis & Lipp, 2001) or without awareness (Davey, 1994; Fulcher & Hammerl, 2005). A meta-
analysis with 50 studies obtained a medium effect size of d = 0.65 for contingency aware
respondents and a low effect size of d = 0.27 for contingency unaware respondents (Nierman,
2008). Similarly, another meta-analysis with 214 studies (Hofmann, De Houwer, Perugini,
Baeyens, & Crombez, 2010) about EC obtained a medium effect size of d = .52 overall. EC
effects were also found to be stronger for high compared to low contingency awareness (d = .52
vs. d = .51, respectively), for supraliminal rather than subliminal US presentation (d = .52 vs. d =
.21, respectively), and for post-acquisition than for post-extinction effects (d = .85 vs. d = .53,
respectively).
Furthermore, EC is also proposed to be goal dependent, and thus may depend on the
goals activated during the process (Dijksterhuis & Aarts, 2010). For instance, compared with
those who were instructed to pay attention to differences, participants who were presented CS-
US pairings and were instructed to pay attention to similarities had larger EC effects (Corneille,
Yzerbyt, Pleyers, & Mussweiler, 2009). In addition, EC also differs from classical conditioning
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in how classical conditioning disappears when the CS is no longer paired with the US (Hamm &
Vaitl, 1996), whereas EC appears to resist extinction, even after 5 and 10 presentations of the CS
without the US (Baeyens, Crombez, Van den Bergh, & Eelen, 1998; Baeyens, Ruiz, & Diaz,
2005; cf. Lipp & Purkis, 2006).
Zajonc (2001) proposed using this model to explain the mere exposure effect, wherein the
repeated stimuli can be seen as the conditioned stimuli, and the lack of aversive experience as the
unconditioned stimulus. According to this account, novel stimuli activate both approach and
avoidance responses. When no aversive experience is paired with the CS, “avoidance and escape
drop out, leaving only approach responses” (p. 226). However, this account cannot explain why
people still increase liking for novel stimuli that may evoke anxiety (Monahan et al., 2000),
suggesting the need for further examination of the conditioning explanation of mere exposure
effects.
Processes Elicited by Persuasive Communications and Influence Factors
Persuasion models
Attitudes are formed and often change following the reception of information contained
in persuasive communications. Trying to convince a friend to see this movie instead of that one,
seeing an ad on the bus urging you to buy a new product, being asked for donations in the street
– persuasive messages play a huge role in our daily lives and in our interactions with others. It is
therefore not surprising that persuasion has been of academic interest for millennia. For instance,
Rhetoric was one of the most influential early Western works. In this work, Aristotle
differentiated logos (appeals to reason, such as facts and arguments), ethos (credibility of and
respect toward the speaker as a persuasive influence), and pathos (emotional appeals).
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Contemporary theorizing about persuasion has often been concerned with understanding
the processing that takes place in response to a communication. One of these theories is the
heuristic-systematic model proposed by Chaiken (1980; Chen & Chaiken, 1999). The model
assumes two qualitatively distinct modes of processing. The first uses readily accessible
information and simple decision rules, such as using the rule what experts say is usually true to
evaluate an advertisement in which a doctor endorses a pharmaceutical brand, and thus, does not
require much effort and is economic. The second one is systematic, requires cognitive effort, and
relies on a more in-depth analysis of the message content. When reliability concerns take
precedence over economic concerns there is more systematic processing (Chaiken, 1980), for
instance, because of the belief that the discussion at hand will have a direct impact on one’s life.
The two processing modes are not assumed to be mutually exclusive (Chaiken, Liberman, &
Eagly, 1989), and are both thought to be executed with the goal of evaluating a message’s
validity (Chaiken, 1980).
The elaboration likelihood model (Cacioppo & Petty, 1984; Petty & Cacioppo, 1981,
1986) is another highly influential model, which shares similarities with the heuristic-systematic
model. It assumes that messages are elaborated on a continuum, and that qualitatively different
processes take place on the two ends of this continuum. The amount of elaboration is determined
by topic factors, such as relevance to one’s own life, person factors, such as need for cognition,
and situation factors, such as distraction. The peripheral route of processing applies when
elaboration is low, and leads to a reliance on heuristics and cues in the environment, as well as
conditioning type processes. When one is watching an advertisement while talking to a friend,
cognitive resources may be focused on the conversation instead of the ad, and the message of the
ad may be evaluated simply by judging whether the source seems like an expert. When people
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are both motivated and able to elaborate a message, however, the central route of processing is
thought to become relevant. People will evaluate the message in more detail, and argument
quality will become a better predictor of persuasion. Attitudes resulting from this process are
assumed to be more resistant to change. For example, waking up with a headache and a runny
nose makes an advertisement about a new cold medication more personally relevant, and thus,
likely to be closely scrutinized. Under certain circumstances, cue information can serve as
argument information (Petty, Wheeler, & Bizer, 1999).
In contrast to these two models, the aptly named unimodel argues that there is just one
process underlying both heuristic or peripheral, and systematic or central, types of processing,
and that there are no qualitative differences between them (Kruglanski & Thompson, 1999;
Kruglanski, Thompson, & Spiegel, 1999). It relates to how knowledge is gained more generally
and is based on the lay epistemic theory (Kruglanski, 1989). If information is linked to the
conclusion via if-then rules, it counts as relevant evidence and can be used in evaluating a
message by constructing syllogisms. This process is assumed to be the same for both cue
information and argument information: Even though they may be qualitatively different in
content, they are processed in the same manner. For instance, the rule if an expert says
something, it’s probably true may be accessible in a person’s mind and the ad itself supplies the
information that an expert says this cold medicine is great. Those two pieces of information are
combined in a syllogism with the conclusion it’s true that this cold medicine is great. Similarly, if
the message contains the argument our cold medicine does not make you drowsy at all and the
viewer believes that if something does not make me drowsy, it is a sign of high quality, this can
be combined in a syllogism with the conclusion this cold medicine is high-quality. Previous
studies that have found that people rely more on the message than on cue information under
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conditions of high elaboration (and vice versa), can be explained by differences in processing
ease based on factors like time, complexity, and length of presentation. In principle, cue
information can be complex (e.g., if source expertise is not clear and has to be inferred via
multiple if-then statements) and message information can be simple. The unimodel is by no
means uncontested and was published alongside several critical reviews (e.g., Ajzen, 1999;
Chaiken, Duckworth, & Darke, 1999).
The cognition-in-persuasion model (Albarracín, 2002) focuses less on the dual-process
versus uni-process debate, in favor of a multi-stage model based on the work by McGuire
(1968). These stages include interpreting information, identifying potentially relevant
information, retrieving additional information from memory, selecting relevant information and
using it to form or update evaluations. Some of these stages can be bypassed in some situations,
for instance, when people infer their attitudes from past behavior and then directly use these
attitudes, without modifying them with situational information. Low cognitive ability or
motivation to process have different effects at different stages of the model. If processing ability
or motivation are low during interpretation, people are more likely to only use easy-to-process
cue information, such as source expertise or current affect (although even this may not occur
when cognitive resources are extremely low; Albarracín & Kumkale, 2003). When cognitive
processing ability and motivation are high, people are more likely to interpret and use
information that is more difficult to process, that is, message arguments. An intervention that
impacts a later stage of processing, for example, by making people enact message-consistent
behavior, is assumed to have a stronger impact on behavior than a message that influences an
earlier stage. In future situations people may use this past behavior as a shortcut to determine that
they have a favorable attitude (cf. Bem, 1965), instead of having to recall, identify as relevant,
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and use factual information from the message. This way, the different stages of processing can be
used to design effective persuasive messages.
Influence Factors
In psychology, the first systematic line of persuasion research was conducted by the Yale
group, which was founded by Hovland and drew on his research with the United States military
during World War II. With the thesis that messages are persuasive when they are rewarding to the
audience, the Yale group (e.g., Hovland, Janis, & Kelley, 1953) studied the impact of message,
source, and recipient factors in the classic question: Who is saying what to whom (Lasswell,
1948, p. 117). These critical variables are reviewed in the upcoming sections.
The Message. The quality of the arguments contained in the message are probably the
most often examined message variable. Argument quality has an explicitly specified role in
different models of persuasion, such as the elaboration likelihood model, where it is primarily
conceptualized as having an influence when message recipients have the ability and motivation
to think about the message arguments (i.e., the central route to persuasion in Petty & Cacioppo,
1986). Other message factors, such as argument length, often affect persuasion even when
message recipients lack the ability and motivation to think about the communication in a careful
manner (Langer, Blank, & Chanowitz, 1978).
There are some specific strategies to frame a message that deserve mention here. The
foot-in-the-door, door-in-the-face, and lowball techniques are all well-known examples of
established persuasion strategies. When first making a small request that the person is likely to
agree to, and then making a larger request, the person is more likely to agree than if the larger
request had been made right away, which is an example of the foot-in-the-door technique
(Freedman & Fraser, 1966). The lowball technique involves a similar procedure where someone
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first agrees to carry out a smaller request, which is then revealed to be more costly than
originally assumed, and typically results in more compliance than if the full behavioral cost had
been presented to start with (Cialdini, Cacioppo, Bassett, & Miller, 1978). The door-in-the-face
technique, in contrast, starts with a large request that is likely to be denied, but then makes
compliance with a later, smaller request more likely (Cialdini et al., 1975). Another strategy that
has received recent attention, especially in the domain of consumer research, is the disrupt-then-
reframe strategy (Davis & Knowles, 1999; Fennis & Stel, 2011). It involves introducing an
element of confusion, such as stating the price of an object in pennies rather than dollars, which
is hypothesized to increase persuasion by a following reframing message or other persuasive
cues (Fennis, Das, & Pruyn, 2006). Potential explanations are that attention is otherwise diverted
(Davis & Knowles, 1999) or that the reframing message satisfies a need for closure (Kardes,
Fennis, Hirt, Tormala, & Bullington, 2007).
An issue that is often debated is the effectiveness of subliminal persuasion, which entails
presenting a persuasive message below the perceptual threshold (i.e., the limen; usually
established by asking people to self-report whether they perceived anything; Cheesman &
Merikie, 1986). Within this debate, it is important to distinguish between two concepts:
Subliminal priming and subliminal persuasion. Subliminal priming refers to a simple stimulus
that can activate concept, whereas subliminal persuasion refers to a specific and more complex
message. There is established evidence showing that subliminal priming effects on behavior do
exist (see Weingarten et al., 2015, for a meta-analysis, d = 0.41, k = 88). For instance, being
subliminally exposed to images of dollar signs increases the probability of making higher bets in
a slot machine game, presumably because the concept of winning was activated (Gibson &
Zielaskowski, 2013). In contrast, complex persuasive messages such as you are thirsty and want
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to buy a drink are very difficult to convey subliminally (much more so when persuaders aim to
increase only beverage sales of one brand without increasing sales of a competing brand).
Research on subliminal persuasion has a history of null findings, failed replications, and
methodological problems (e.g., Beatty & Hawkins, 1989; De Fleur & Petranoff, 1959; Moore,
1988; Pratkanis & Aronson, 1992; Smith & Rogers, 1994). In general, when subliminal
persuasion effects do emerge they seem to be moderated. There are some findings of main effects
(Cooper & Cooper, 2002; see also Trappey, 1996), including the infamous, and fraudulent,
reports of increased Coca-Cola and popcorn consumption after subliminal messages in a movie
(see McConnell, Cutler, & McNeil, 1958). The most commonly accepted conclusion seems to be
that subliminal persuasion has an effect only when an underlying base motivation already exists.
For instance, being primed with thirst-related words only has an effect on people who are thirsty
to start with (Strahan, Spencer, & Zanna, 2002). Another moderator seems to be interpretation of
the subliminal stimulus which can be quite variable. The word dad, for example, only increases
achievement behavior in individuals who feel close to their dad and believe their dad to value
achievement (Fitzsimons & Bargh, 2003). A more indirect form of subliminal persuasion has also
been documented in relation to source effects: Participants who had been subliminally exposed to
the communicator's face were later more persuaded by the arguments that this communicator
brought forth (Weisbuch et al., 2003). Overall though, evidence for direct and reliable subliminal
transmission of complex messages is weak.
Finally, a message type that has been gaining popularity is the narrative. Narrative
persuasion typically uses fictional stories in which target issues are discussed, or in which
characters act in accordance with target behaviors. Experience taking (identifying with and
simulating a characters’ inner experience; Kaufman & Libby, 2012) and, more generally,
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transportation into the story (see van Laer, de Ruyter, Visconti, & Wetzels, 2014), seem to be
important mechanisms of narrative persuasion. When readers are transported into the story and
identify with characters, their attitudes align more with those of the character or the narrative in
general (e.g., de Graaf, Hoeken, Sanders, & Beentjes, 2012; Kaufman & Libby, 2012; Mazzocco,
Green, Sasota, & Jones, 2010; Murphy, Frank, Chatterjee, & Baezconde-Garbanati, 2013), in
part because the narrative facilitates attention to the message and elaboration of its contents
(Igartua & Barrios, 2012; Slater & Rouner, 2002).
The Recipient. Who is receiving the message? A host of interindividual differences have
been studied regarding this question (for a review, see Briñol & Petty, 2005), and Briñol and
Petty (2005) outline categories that influence persuasion: Individual differences related to
knowledge (e.g., need for cognition and need to evaluate, see below), consistency (e.g.,
authoritarianism; Altemeyer, 1981), self-worth (e.g., self-esteem; Rosenberg, 1979), and social
approval (e.g., individualism versus collectivism; Triandis, McCusker, & Hui, 1990). Among
these, two personal characteristics have been especially tightly linked to attitudes and theories of
persuasion. The first one is need for cognition, which Cacioppo and Petty (1982) defined as a
tendency to engage in and enjoy thinking (p. 116), drawing on early research from the 1940’s and
1950’s (e.g., Cohen, Stotland, & Wolfe, 1955). People high in need for cognition look for, and
elaborate on, more information (Curşeu, 2011; Verplanken, Hazenberg, & Palenéwen, 1992), and
show higher attitude extremity under specific circumstances (Leone & Ensley, 1986; Smith,
Haugtvedt, & Petty, 1994). Need for cognition is assumed to exert a chronic motivational
influence, making individuals high in need for cognition more likely to use central information
processing (Petty & Cacioppo, 1986).
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The other individual difference is need to elaborate, or the extent to which [individuals]
chronically engage in evaluative responding (Jarvis & Petty, 1996, p. 172). It describes the
propensity to form attitudes, and leads to more commitment to attitudes (Maxwell-Smith &
Esses, 2012). In the discussion of whether attitudes are formed on-line or are based on retrieval
from memory, need to evaluate may be a moderator such that people high in need to evaluate are
more likely to form an on-line judgment (Tormala & Petty, 2001). In contrast, people who are
less invested in evaluating things are more likely to retrieve prior judgments from memory. Need
to evaluate, too, has sometimes been used as an indicator of a person’s motivation to engage in
effortful processing of attitude-relevant information (e.g., Lenzner, 2012).
Another individual difference variable that has been studied extensively in persuasion,
especially in consumer research, is regulatory focus (Higgins, 1997). People can be more
oriented towards promotion, or they can be more oriented towards prevention. This orientation
can affect not only goal pursuit (e.g., Crowe & Higgins, 1997), but also processes that are more
closely related to attitudes. For instance, information selection (e.g., Werth & Foerster, 2007) and
favorable responses to persuasive messages (e.g., Cesario, Corker, & Jelinek, 2013; Keller, 2006)
are impacted by an individual’s regulatory focus. In most cases, the fit between a promotion
versus prevention focus of the person and the message has been investigated, and a closer fit has
been found to increase persuasion (e.g., Cesario et al., 2013; Latimer et al., 2008; Yi &
Baumgartner, 2009). Cesario, Grant, and Higgins (2004) found that participants with a successful
history of promotion-related actions were more likely to be persuaded by a promotion-framed
message (e.g., this program will support more children to succeed) than by a prevention-framed
message (e.g., this program will prevent more children failing), and the reverse was true for
participants with a successful history of prevention-related actions. This regulatory matching
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effect was driven by the misattribution of the fluency feeling (i.e., feeling right) produced by the
match, and disappeared when people attributed their feeling to another cause. Contrastingly,
Malaviya and Brendl (2014) found a reversal regulatory matching effect when participants
perceived a hedonic mismatch (e.g., perceiver’s attention was focused on painful outcomes but
the message was focused on pleasurable outcomes, or vice versa). Participants were presumably
alerted to the hedonic mismatch and felt skeptic about the message.
Recently there has also been interest in a person’s own perceptions of their own attitudes.
Whether an attitude is based on affective or cognitive information has previously been found to
predict which types of persuasion are most successful (Edwards, 1990; Fabrigar & Petty, 1999).
In addition to the objective bases of an attitude, a person’s subjective assessment of whether it is
affectively or cognitively based (meta-basis), influences information selection and persuasion
(See, Petty & Fabrigar, 2008, 2013). Another type of self-perception that potentially affects
persuasion relates to the perceived stability of one’s attitudes. These implicit theories of attitudes
may predict attitude certainty following attitude change, or lack thereof, after receiving a
persuasive message (Petrocelli, Clarkson, Tormala, & Hendrix, 2010).
Ironically, being sure of your opinion can make you more likely to change it – as a
consequence of defensive confidence (Albarracín & Mitchell, 2004). If you are very confident in
your opinion and your ability to defend it, you have no reason to avoid counter-attitudinal
information and may even seek it out on purpose. However, strong counter-attitudinal messages
continue to exert their effect and result in persuasion. Defensive confidence is conceptualized as
an individual difference that is not topic-specific and predisposes certain individuals to this
unintended side effect of certainty. Interventions targeting traditionally hard-to-reach audiences
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can employ knowledge about defensive confidence to change strongly endorsed attitudes
(Albarracín, Mitchell, Durantini, Earl & Levitt, 2007).
The Source
A third category of variables in persuasion describes who is sending the persuasive
message. A classically studied variable, and one that explains more variance than any other
widely studied source variable (Wilson & Sherrell, 1993), is the ostensible expertise of the
source. Other well-known source effects include effects of likeability, physical attractiveness,
perceived credibility, and trustworthiness (see Pornpitakpan, 2004; Wilson & Sherrell, 1993).
French and Raven (1959) conceptualized different types of power that a communicator may
possess that may influence persuasion, which includes expert power (expertise) and referent
power (likeability), but also reward power and coercive power, which allow the person to assign
rewards and punishments, and finally legitimate power, or possession of the right to influence
others. Interestingly, not only does the power of the source seem to matter, but lower power of
the message recipient can also predict higher reliance on source characteristics in persuasion
(Albarracín & Vargas, 2010; Durantini, Albarracín, Mitchell, Earl, & Gillette, 2006).
Source variables in general are assumed to mostly serve as heuristic cues in theories of
persuasion, such as the heuristic-systematic model or the elaboration likelihood model (e.g.,
Petty & Cacioppo, 1984). Nevertheless, it would be wrong to assume that source variables only
ever have such straightforward, direct effects. Sources have a larger effect when forming an
evaluation of a new attitude object, but a weaker one when an established attitude exists already
(even though this advantage may decay over time; Kumkale, Albarracín, & Seignourel, 2010),
the effect of source variables depends on the time of presentation (e.g., Campbell, Mohr, &
Verlegh, 2013; Tormala, Briñol, & Petty, 2007), and there is evidence of bidirectional effects that
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change perception of the source depending on message content (e.g., Gawronski & Walther,
2008). In addition, sleeper effects in persuasion can have more complex long-term consequences.
When both a message and discounting source information such as this person cannot be trusted
are presented, there can be little or no net persuasion at first, but delayed persuasion over time,
showing a long-term impact of the message despite the discounting information (Hovland,
Lumsdaine, & Sheffield, 1949; Hovland & Weiss, 1951; Kumkale & Albarracín, 2004).
Finally, it is useful to differentiate between laboratory source effects and real-life source
effects. In laboratory studies, source effects are necessarily limited. Typically, manipulations are
simplistic and circumvent direct interaction completely (e.g., Clark, Wegener, Habashi, & Evans,
2012; Homer & Kahle, 1990; Klein & DeBono, 1993; McGinnies & Ward, 1980; Tormala &
Petty, 2004), for example, by saying that an article was written by a leading scholar in the field
versus a high school junior (Clark et al., 2012). Even in controlled field studies, the
communicators are required to stick to a close script. Despite the benefits of such procedures for
establishing internal validity, real-life communicators, and real experts in particular, do more
than what is on the script. Research on real-life behavioral health interventions show that experts
and demographically-similar sources consistently elicit much more behavior change than do lay
community members and demographically-dissimilar sources (with exceptions for select
demographics, e.g., teenagers changed their behavior more in response to sources that were
similar to them than to experts; Durantini et al., 2006).
Processes Underlying Attitude Change
Social Judgment Theory
According to the social judgment theory (Sherif & Hovland, 1961; but see Eiser, 1973;
Eiser & Mower White, 1974; for a review, see Eagly & Chaiken, 1993), attitude change is the
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result of a perceptual process. When the position of the communication is close to the recipients’
attitude, people become closer to the position advocated in the communication by assimilating
their own attitude to the advocacy. In contrast, when the communication is subjectively distant
from their attitudes, there is a contrast effect or perception that one’s attitude is more discrepant
from the communication than it actually is. In these situations, people change in opposition to the
communication.
Several other predictions of the social judgment theory concern the conditions leading to
contrast versus assimilation. A chief assumption is that attitude change is a function of the range
of positions a person accepts and rejects. When the message position falls within this latitude of
acceptance, people assimilate this position to their attitudes. When the position falls within the
latitude of rejection, people contrast their attitudes with that position. Furthermore, topics that are
highly involving shrink the latitudes of acceptance and expand the latitudes of rejection. As a
result, people are more resistant to change (Johnson & Eagly, 1989, 1990; Johnson, Lin, Symons,
Campbell, & Ekstein, 1995; Lampron, Krosnick, Shaeffer, Petty, & See, 2003). Although
interesting, this prediction has not received consistent support over the years (Eagly & Chaiken,
1993; Johnson et al., 2005).
Selective Exposure
Despite the fact that strong evidence and arguments are ubiquitously used in an attempt to
change recipients’ attitudes, persuasion often fails (The Smoking Gun, 2006). One cause of the
difficulty changing attitudes is the process of selective exposure, which enables people to defend
their attitudes by avoiding information likely to challenge them, and seeking information likely
to support them. This tendency is a form of congeniality bias, which describes people’s
motivation to defend their attitudes from challenges (e.g., Festinger, 1957; Olson & Stone, 2005).
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A meta-analysis that assessed whether exposure to information is guided by defense or accuracy
motives (Hart et al., 2009; see also Noguchi, Durantini, Albarracín, & Glasman, 2007) showed
that, although analyses indicated a moderate preference for congenial over uncongenial
information (d = 0.36), this congeniality bias was moderated by variables that affect the strength
of the defense and accuracy motivations. In support of the importance of defense motivation, the
congeniality bias is weaker when people have a weaker defense motivation. Low defense
motivation is assumed to occur when participants’ attitudes, beliefs, or behaviors are supported
prior to information selection, when participants’ attitudes, beliefs, or behaviors are not relevant
to important values or not held with conviction, when the available information is low in quality,
when participants’ closed-mindedness is low, and when confidence in the attitude, belief, or
behavior is high.
Models of Belief Correction as Models of Attitude Change
Attitude and belief change is a more complex and difficult undertaking than implied in
models of persuasion (Cook et al., 2013; Greitemeyer, 2014; Honda, Shimizu, & Rutter, 2005).
For example, the program AdWatch was first initiated in 1992 by several national news networks
to filter out political commercials with misleading or deceptive advertising during the
presidential campaign. If attitude change were easy, providing information about deceptive
advertising should suffice to restore attitudes to baseline levels. A field experiment showed,
however, that Adwatch failed to change attitudes towards presidential candidates even for
audiences that understood and even liked receiving reports of the deception (Cappella &
Jamieson, 1994).
A simple survey of some common misconceptions also highlights how resistant to change
beliefs and attitudes are. Despite substantial evidence of human-induced global warming, a
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The Psychology of Attitudes and Persuasion
recent poll showed that only 55% of Americans believe that climate change stems from human
activity, down from 61% in 2001 (Cook et al., 2013; Gleick et al., 2010; Oreskes, 2004; Saad,
2015). Another good example is the persistent belief in the association between vaccines and
autism (Smith, Ellenberg, Bell, & Rubin, 2008). In 1998, Wakefield and his colleagues published
a paper in The Lancet asserting that the MMR (Measles, Mumps, and Rubella) vaccine caused
autism in children. Although there were abundant questions about the results (e.g., Fombonne &
Cook, 2003; Honda, Shimizu, & Rutter, 2005), methodological problems (e.g., Chen &
DeStefano, 1998), and an actual retraction of the paper in 2010, misbeliefs about the vaccine and
refusals to vaccinate continue to drive high incidence of those diseases (Center for Disease
Control and Prevention, 2015). The lack of impact of the retraction, however, is consistent with
the finding that more than two hundred retracted articles were cited 2,034 times after the
retraction, usually with no mention of the retraction (Budd, Sievert, & Schultz, 1998). The
suggestion of such a high number of citations has been confirmed by experimental research
showing that post retraction readers of a retracted article are more likely to believe the findings
than readers of control research articles (Greitemeyer, 2014).
What explains this persistence of beliefs? Why do corrections and retractions so often fail
to change attitudes and beliefs? To begin, adults rarely consider an issue or object in the absence
of any prior relevant information, so a priori attitudes and goals play a large role in guiding
processing. As argued by Kunda (1990), people arrive at conclusions at which they want to arrive
to the extent that they can construct seemingly reasonable justifications for such conclusions. A
study conducted by Kunda and Sanitioso (1989) showed that thinking that a given trait (e.g.,
extroversion) was associated with academic success led participants to perceive themselves as
having a higher level of that trait (e.g., being more extroverted than the norm). Furthermore, the
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The Psychology of Attitudes and Persuasion
belief that caffeine consumption has negative health consequences led people to report less
coffee drinking as a way of feeling less threatened (Sherman & Kunda, 1989), and people who
perceived introversion as desirable could generate memories that suggested they were introverted
faster than those who thought extraversion was desirable (Sanitioso, Kunda, & Fong, 1990).
A motivated cognition mechanism suggests that attitudes and beliefs that are consistent
with other valued attitudes and beliefs should increase persistence. First, defensive cognitive
processes can be activated to protect the attitudes and beliefs, particularly when self-protective
motivation is high and accuracy motivation is low (see Hart et al., 2009). A higher, perhaps even
unreasonable, evidential threshold of information is often required to refute cherished attitudes
and beliefs (Ditto & Lopez, 1992). Motivated cognition, however, is not the only process that
explains attitude and belief persistence. Ecker and colleagues (2014) first presented a crime
scenario in which the suspect was initially described as an Australian Aboriginal. The
information about the ethnicity of the suspect was later corrected, and the degree of belief change
was not correlated with preexisting racial attitudes towards Aboriginals. In addition to this null
effect, there is large amount of evidence of attitude and belief persistence in domains that are
personally irrelevant. For example, whether a fictional fire is described as caused by arson or by
accident is of no relevance to most experimental participants. Yet recipients of this information
form beliefs that persist even in the presence of subsequent correction (Johnson & Seifert, 1994).
The cognitive processes of anchoring and adjustment (Tversky & Kahneman, 1974) are
important to understand the difficulty changing attitudes and beliefs. Initial attitudes and beliefs
can be conceptualized as setting an initial anchoring position and correction requires adjustment
away from that initial position. Therefore, both under-correction and over-correction are possible
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The Psychology of Attitudes and Persuasion
because accurate adjustment requires knowledge about the extent of the influence of
misinformation (Wilson & Brekke, 1994).
Epley and Gilovich (2006) have suggested that under-correction, or insufficient
adjustment, is more prevalent than over-correction because people tend to adjust incrementally
from a lower self-generated value and terminate their adjustment once a plausible value is
reached. Unless individuals are willing to search for a more accurate value, under-correction is
likely the outcome that in turn explains the continued influences of misinformation. Previous
studies of judgment adjustment have mainly focused on under-correction (for a review, see
Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001; Rozin & Royzman, 2001). A recent study by
Cobb and colleagues (2013) found that both under- and over- corrections are possible depending
on the type of information that is presented. For example, when it comes to correcting positive
misinformation about political figures, message recipients overestimate how much correction is
needed and end up with excessively negative attitudes towards the politician.
There are various other cognitive factors leading to this persistence, including generation
of explanations about the information. The tendency to generate reasons why the misinformation
might hold true tends to increase persistence, whereas the generation of alternatives reduces
persistence (e.g., Anderson, 1982; Seifert, 2002). Generating counterarguments depends not just
on actual consideration of opposite beliefs, but also metacognitive experiences that accompany
the reasoning process (Schwarz, Sanna, Skurnik, & Yoon, 2007). The ease or difficulty with
which information can be processed is one of these metacognitive experiences. Aarts and
Dijksterhuis (1999) found that people infer that they use their bicycles more often after recalling
few than many occasions, suggesting that people are likely to believe in what relevant
information can be easily brought to mind. As a result, generating arguments against prior beliefs
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and attitudes can be more effective when it is easy and fluent, than when it is difficult
(Lewandowsky, Ecker, Seifert, Schwarz, & Cook, 2012; Nestler, 2010). Another metacognitive
experience is the ease or difficulty with which new information can be processed. Pieces of
information that are incongruent or inconsistent with prior beliefs are processed less fluently than
those that are consistent with one’s beliefs (Winkielman, Huber, Kavanagh, & Schwarz, 2012).
Fluency indicates congruency, and information that can be processed fluently feels more familiar
and is thus likely to be viewed as true and elicit less scrutiny (Lev-Ari & Keysar, 2010; Schwarz
et al., 2007; Song & Schwarz, 2008).
People tend to engage in confirmatory or positive hypothesis testing, preferentially
considering reasons for an assertion that might be true rather than false (Klayman & Ha, 1987).
Individuals are thus likely to elaborate plausible premises even if they are false, and this
confirmatory fallacy decreases our ability to edit our prior attitudes and beliefs (Johnson &
Seifert, 1994; Seifert, 2002). People, however, are more likely to falsify attitudes and beliefs
when supportive arguments are difficult to come by (Anderson, 1982; Johnson & Seifert, 1994;
Lewandowsky et al., 2012; Nestler, 2010).
Prior theories of thinking and reasoning also sheds light on the cognitive processes
leading to the revision of, or the frequent failure to revise, beliefs. A mental model of reasoning
(Johnson-Laird, 1994; Johnson-Laird & Byrne, 1991), for example, states that people construct a
web of mental models from which they can derive causal conclusions. As new information
unfolds, people set up new models or extend existing models, but are unwilling to discard key
information when no plausible alternative exists to fill the gap (Johnson & Seifert, 1994; Wilkes
& Leatherbarrow, 1988). Therefore, providing a causal alternative to fully explain mental models
facilitates belief revision but corrections often fail to explain events. For example, information
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about the reasons for global warming should facilitate belief change whereas mere denials of
misconceptions are inadequate at producing change.
Another theory that accounts for belief perseverance was formulated within a dual-
process framework. System I includes fast, instinctive, and emotional reasoning, whereas system
II involves slower, more deliberative, and more rational reasoning (for reviews, see Croskerry,
Singhal, & Mamede, 2013; Kahneman, 2003). Consistent with this model, Kowalski and Taylor
(2009) demonstrated that a controlled and careful dissection of incorrect ideas facilitates the
acquisition of correct information. In a naturalistic experiment, the direct refutation to false
information was more successful in changing attitudes and beliefs, than was the non-refutational
provision of the same information. Furthermore, in-depth processing of information and
corrections may assist people in working through inconsistencies and ultimately accept the
corrections (Osborne, 2010).
Further understanding of belief and attitude persistence comes from fuzzy-trace theory, a
dual-trace conceptualization of reasoning (Reyna & Brainerd, 1995). According to this model,
people tend to process gist information – fuzzy representations often activated automatically –
instead of the verbatim information – the detailed features – presented in a message. Thus, gist
and detailed information associated with a belief compete for memory retrieval, with the gist
information being faster (Ayers & Reder, 1998). Therefore, beliefs persevere because details,
such as the falsification, are not strategically retrieved but the gist (e.g., the story) is interesting
and memorable.
Last but not least, the normative theory of Bayesian inference has also been used to
elucidate belief persistence (Griffiths, Tenenbaum, & Kemp, 2012). Bayesian models capture
causal relations among (potentially) a full set of variables and provide a way of finding hidden
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constraints that explain belief revision (Jern, Chang, & Kemp, 2009; O’Connor, 2006). Jern et al.
(2009) presented a model of belief polarization based on a fully Bayesian approach to belief
revision. In this case, initial beliefs act as priors that bias consideration of all evidence and can
lead to polarization, rather than mere maintenance. With the consideration of all (both apparent
and non-apparent) variables, such an approach can provide a comprehensive view of a behavior
that one might consider irrational, as actually a rational behavior with non-apparent (hidden)
variables taken into account. Therefore, the failure to change attitudes and beliefs may arguably
represent a normatively rational inference of prior biases with new information..
Not surprisingly, cognitive competence and preparedness capacity also increase the
probability of changing attitudes and beliefs (Calvillo, 2014; Chambers & Zaragoza, 2001; Zhu
et al., 2010). In a study of memory capacity and belief construction conducted by Zhu and
colleagues (2010), performance on general memory tasks was negatively associated with
adherence to false beliefs. Further, people are likely to revise their beliefs after corrections if they
are explicitly warned up front that information they are about to be given may be misleading
(Chambers & Zaragoza, 2001; Ecker, Lewandowsky, & Tang, 2010; Jou & Foreman, 2007).
Warnings seem to be more effective when they are given before specific beliefs and attitudes are
encoded rather than after, because such warnings prepare recipients to monitor the encoded input
and label potential cues as suspect (Chambers & Zaragoza, 2001; Ecker et al., 2010; Schul,
1993).
In addition to aforementioned factors, recent studies have demonstrated that
conspiratorial thinking predicts the persistence of erroneous beliefs. Conspiratorial thinking, or
ideation, refers to the cognitive tendency to explain a significant political or social event as a
secret plot by powerful individuals or organizations (Sunstein & Vermeule, 2009). Conspiratorial
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The Psychology of Attitudes and Persuasion
thinking predicted the rejection of scientific findings, including the well-established climate
science (Lewandowsky, Oberauer, & Gignac, 2013). Apparently, people with conspiratorial
thinking lack a priori assumptions about what constitutes a scam and cannot question or avoid
introducing their own conspiracy theories (Lewandowsky et al., 2015).
Finally, recent studies of attitude and belief have demonstrated that the emotional tone of
the information affects change (Porter, Bellhouse, McDougall, ten Brinke, & Wilson, 2010; Van
Damme & Smets, 2014). Compared to positive images, negative images were associated with a
greater susceptibility to false memories for a major misleading detail at subsequent follow-up
sessions. Therefore, negative emotions apparently make the information more difficult to change
even after attempts at correction.
Models of Resistance to Persuasion
Perceiving persuasive intent is generally sufficient for a host of resistance processes to
develop. Audiences abandon the communicator and avoid similar messages in the future
(Festinger, 1964; Hart et al., 2009) and actively counter-argue the message to resist its influence
(Sagarin & Cialdini, 2004; Wegener & Carlston, 2005). Interestingly, the initial research on
attempts to counter an external influence had the objective of studying experimental participants
who act contrary to the experimenter’s hypothesis (Weber & Cook, 1972). Masling (1966)
described this effect as a participant’s screw you reaction to the suspicion that the experimenter
was attempting to control their minds. In a classic set of two studies (Christensen, 1977),
participants were induced to suspect, actually experience, or neither suspect nor experience being
victims of psychological manipulation. In the suspicion condition, the experimenter indicated
that psychological experiments often include procedures to manipulate behaviour without the
participants’ knowledge. In the experience condition, participants were asked to copy telephone
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The Psychology of Attitudes and Persuasion
numbers. While participants wrote numbers, the experimenter told them that fast copying was
indicative of an obsessive-compulsive personality disorder, which they later learned was a
calculated strategy to jeopardize their performance. As a control, the third condition had neither
instructions nor experience manipulations. Participants in these three conditions then underwent
a verbal conditioning task (Taffel, 1955) during which the experimenter attempted to condition
some responses by repeating good. The experimenter’s statements would normally increase the
occurrence of the reinforced responses, but participants’ resistance may actually eliminate this
effect. As expected, participants showed conditioning only when they did not have a recent
manipulative experience.
The fascinating phenomenon of resistance to an external influence has received the
attention of social psychologists from the beginning of the study of communication and
persuasion. For example, McGuire (1964) directly manipulated the motivation to self-defend
from a persuasive message by having participants counter-argue weak forms of the message
before a subsequent stronger attack. According to him, the process of counter-arguing should not
only increase refutational ability, but also the motivation to self-defend from the attack. Although
the two processes could not be disentangled in McGuire’s work, the refutational practice did in
fact decrease vulnerability to later attack.
Recent work by Sagarin, Cialdini, Rice, and Serna (2002) has advanced our
understanding of the role of motivation in defending our attitudes against influence attempts.
Participants received training to discriminate between legitimate and illegitimate sources based
on whether the source had expertise in a particular domain. This treatment was designed to make
participants aware that there was a potential influence source, able to discriminate between
legitimate and illegitimate sources, and also willing to make this discrimination. As predicted,
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The Psychology of Attitudes and Persuasion
the treatment increased persuasion for legitimate sources but decreased it for illegitimate ones.
Other studies of the same series confirmed that the perception of manipulation decreases
persuasion both directly, and by mediating influences on specific counter-arguments of the
message content (see also Schul, Mayo, & Burnstein, 2004).
Sources of influence may also be rejected when they are perceived as being hypocritical.
Over 1,500 inner-city high school students were assigned to different conditions of an HIV-
prevention-intervention trial: the key conditions were a teacher-led intervention and a student-led
intervention (Fisher, Fisher, Bryan, & Misovich, 2002). Findings revealed that, compared to the
teacher-led intervention, the peer-led intervention was more effective at the 3-month follow up
but less effective at the one-year follow up. Apparently, over the course of a year, the student
interventionists were perceived as hypocritical because they displayed some of the behaviors
they had previously tried to discourage in their peers. This perception of the source as
hypocritical presumably decreased the effectiveness of the peer-led intervention.
Another interesting facet of reacting against a seemingly active environment is that the
influence source provides an anchor for reflecting about attitudes. Rucker and Petty (2002)
presented participants with a strong ad promoting a pharmaceutical product and instructed
participants either to list negative thoughts (for an introduction to the technique of induce biased
thoughts, see Killeya & Johnson, 1998), or to simply list their thoughts about the message.
Presumably, participants who listed only negative thoughts attempted to resist the
communication to a greater extent than those who were free to list any (positive, negative, and/or
neutral) thoughts. Findings indicated that participants were persuaded regardless of what
thoughts they listed, probably because the ad was difficult to refute. However, participants who
attempted to resist the message (and failed) were more confident in their favorable attitude
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toward the product than those who did not make an effort to resist persuasion. In other words,
confidence was established in relation to prior reactions to the message.
Research conducted by Albarracín, Cohen, and Kumkale (2003) also suggests that
message recipients consider the degree to which a prior message was persuasive. In this research,
participants received a message that recommended either abstinence from, or moderation in, the
use of a new type of alcohol product. After reading these materials, participants either tried the
product or performed a filler task before reporting their intentions to drink in the future. As
predicted, participants who did not try the product reported similar intentions to drink when they
received the moderation message and when they received the abstinence message (d = .03). In
contrast, when participants tried the product after receiving the message, recipients of the
abstinence message had significantly stronger intentions to drink than recipients of the
moderation message (d = .76). One interpretation of these results is that participants inferred
their intentions after considering their trial behavior vis-à-vis an external influence (the message
recommendation). Apparently, trying the product after a strong recommendation led participants
to conclude that they truly liked the forbidden product. Another potential interpretation is that the
discrepancy made people anxious and thus more likely to resolve these feelings by justifying
their drinking behavior (Festinger and Carlsmith, 1959). Although this particular interpretation
seemed less plausible than the self-perception account based on additional data, both processes
are relevant to situations in which the recipients’ behavior contradicts an earlier persuasive
message.
Reactions to perceived influence intent are in many ways similar to phenomena identified
in the context of cognitive dissonance. In Festinger and Carlsmith’s (1959) classic study, male
participants worked for one hour on boring tasks such as turning spools on a board. Immediately
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after this task, participants were told that the experimenter was investigating the effects of
expectancies on performance. They were further informed that they were in a control condition
that did not receive any information before beginning the tasks. However, they learned that other
participants were to receive information designed to create a positive expectation about the task.
The positive expectations were ostensibly instilled by asking the participants to tell another
student that the task was enjoyable. Importantly, participants were offered either $1 or $20 for
providing a positive evaluation of the task to the other participant (actually an experimenter’s
accomplice). Results of how enjoyable the task was to the participants depended on the amount
of money they received in exchange for lying. Participants who were paid $1 for describing the
experiment as enjoyable rated the tasks as more enjoyable than did participants who were paid
$20. Festinger and Carlsmith (1959) argued that participants who lied experienced dissonance
created by the cognitions The tasks were boring and I told someone the tasks were enjoyable.
Those who were paid $20, however, had an important consonant cognition in I was paid a lot of
money to tell someone the tasks were enjoyable. This awareness of an environmental influence
was sufficient to reduce the dissonance magnitude and the associated influence of the behavior
on private reports.
Cognitive dissonance theorists give arousal and perceived self-determination central roles
in attitude change (Olson & Stone, 2005), and highlight how awareness of a reward’s influence
can increase the level of arousal associated with an object. In particular, arousal measured with
skin conductance is greater when participants undergo typical dissonance manipulations and
have no opportunity to reduce this dissonance (Croyle & Cooper, 1983; Elkin & Leippe, 1986).
This finding has received repeated support (see Harmon-Jones, Brehm, Greenberg, Simon &
Nelson, 1996) and is generally taken as an indication that affective feelings are necessary for the
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experience and consequences of cognitive dissonance. Even more definitive, however, is
evidence from studies using misattribution paradigms (Zanna & Cooper, 1974). For example, in
one study participants engaged in belief-discrepant behavior, and for some, physiological
measures of arousal were obtained (Croyle & Cooper, 1983). When physiological measures were
obtained, participants’ beliefs were not affected by their behavior, presumably because they
attributed their experienced arousal to the elaborate measurement apparatus. In contrast, when no
such measures were obtained, participants supposedly attributed their feelings to the behavior
and changed their beliefs accordingly. Participants were either instructed to write a counter-
attitudinal essay (low choice), or politely asked to write a counter-attitudinal essay (high choice).
Further, all participants were given a placebo tablet. Some were informed that the pill would,
“produce a reaction of tenseness,” others that the pill would, “produce a reaction of relaxation,”
and others that the pill had no side effects (p. 705). Participants in the low choice conditions
showed minimal attitude change – for low choice participants, writing the essay was not a
counter-attitudinal behavior because they were able to make an external attribution for their
dissonant behavior (e.g., “the experiment required that I write that essay”). Participants in the
high choice conditions showed varying degrees of attitude change. Those in the “tenseness”
condition showed minimal attitude change – they, too, were able to make an external attribution
(to the placebo) for the physiological arousal induced by their dissonant behavior. Those in the
“no side effect” condition showed moderate attitude change, consistent with other research on
cognitive dissonance. And those in the “relaxation” condition showed a great deal of attitude
change – they “showed an increased need to deal with their arousal by changing their opinions,”
(p. 707).
Goals and Attitude Change
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The Psychology of Attitudes and Persuasion
A variety of motives and goals (for a review, see Kunda, 1990) can potentially influence
the formation and change of attitudes. These goals include the motivation to be accurate
(Kruglanski, 1980), to be consistent (Festinger, 1957; Heider, 1946; McGuire, 1960), to believe
in a just world (Lerner, Miller, & Holmes, 1976), to avoid uncertainty and ambiguity (Harvey,
Hunt, & Schroder, 1961; Roney & Sorrentino, 1995), and to avoid engaging in excessive
cognitive effort (Chaiken, 1987; Taylor & Fiske, 1978). Once a goal is activated, it may direct
people’s attention to goal-consistent information. For instance, people who are motivated to
defend and rationalize the status quo prefer positive information about the status quo, whereas
people who are motivated to improve the status quo prefer negative information about the status
quo (Johnson & Fujita, 2012).
Beyond specific goals, general behavioral goals, once activated, may have a surprising
and powerful impact on attitude change. General action goals – defined as the goals with end
states at the extremes of the continuum of activity level (either high or low motor and cognitive
output) – have been found to affect judgment and behavior across a variety of domains
(Albarracín et al., 2008; Albarracín, Wang, & Leeper, 2009; Gendolla & Silvestrini, 2010; Laran,
2010; for reviews, see Albarracín et al., 2008, 2011). For example, participants primed via
subliminal or supraliminal exposure to such action-related words as move, go, and walk,
compared with those primed by such inaction-related words as stand, still, and calm, showed
higher level of cognitive and physical activities including doodling (vs. napping), exercised for a
longer time, ate more raisins, and solved more anagrams. In a similar vein, implicit activation of
the action concept has been linked to stronger effort-related cardiovascular responses than
activation of the inaction concept (Silvestrini & Gendolla, 2013). Moreover, the activation of
action goal may increase cognitive effort when individuals are confronted with persuasive
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messages, which may bias the process of the information selection. In particular, as people
generally have a prior attitude toward object or topic, the activation of action goal may facilitate
retrieving the prior attitude and may in turn inhibit attitude change. This possibility has been
tested by Albarracin and her colleagues. Compared with participants primed with general
inaction goals, those primed with general action goals were found to quickly and consciously
retrieve prior attitudes, which hindered their attitude change. This effect, however, is attenuated
or reversed when the goals have already been satisfied by an intervening task (Albarracin &
Handley, 2011). Future research examining this possibility may be interesting. Furthermore, the
implication of the general action and inaction goals on attitude change in various applied
contexts such as marketing and communication may also be important.
Models of the Attitude-Behavior Association
There is abundant research indicating that general attitudes are good predictors of broad
patterns of behavior, with correlations ranging from .63 (Fishbein & Ajzen, 1974) to .73
(Bandura, Blanchard & Ritter, 1969). In contrast, the association between general attitudes and
individual behaviors is weak, such that general attitudes are only infrequently, and inconsistently,
able to predict specific behaviors (Ajzen, 2012; Ajzen & Fishbein, 2005). Models of the
association between attitudes and behavior provide a framework for understanding when and
how attitudes are predictive of behavior. Therefore, while attitudes are often of interest because
of their relation to behavior, further understanding the strength of that association and the
psychological pathways underlying it, are important.
Theory of Reasoned Action and Theory of Planned Behavior
According to the theory of reasoned action (TRA; Ajzen & Fishbein, 2005; Fishbein &
Ajzen, 1975), behavior can be predicted from an individual’s intention to perform the behavior.
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Intentions are in turn predicted from attitudes toward the behavior and subjective norms. The
attitude towards the behavior is the individual’s evaluation that the behavior is desirable or
undesirable. The subjective norm is the perceived normative pressure to perform the behavior.
Both of these components are predicted from salient beliefs.
The attitude towards performing a behavior is determined by the subjective values or
evaluations of the outcomes associated with the behavior and by the strength of these
associations. Specifically, the evaluation of each outcome contributes to the attitude in direct
proportion to the person’s subjective probability that the behavior will lead to the outcome in
question. The basic structure of the model is shown in the equation below, where AB is the
attitude toward the behavior, bi is the strength of the belief that the behavior will lead to outcome
i, ei is the evaluation of outcome i, and the sum is over all salient outcomes (see Fishbein &
Ajzen, 1975).
AB = Σbiei
The subjective norm depends on a set of normative beliefs. Normative beliefs comprise of
expectations that important social referents, like the person's family, support the behavior. These
normative beliefs can be combined with the motivation to comply with the referents to predict
subjective norms regarding the behavior.
The TRA assumed that individuals have control over all of their behaviors, and thus, was
not designed to explain behaviors performed outside an individual’s volitional control. The
theory of planned behavior (TPB; Ajzen, 1991) was consequently developed as an extension of
the TRA. Similar to the TRA, the TPB is an expectancy value model that describes the relation
between attitudes, subjective norms, behavioral intentions, and behaviors. However, it improves
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the TRA by including perceived behavioral as another factor that can facilitate or impair
performance of a given behavior.
Perceived behavioral control reflects one’s perceptions that one can engage in a behavior
if one wants to (Ajzen, 1991). It is closely related to self-efficacy (Bandura, 1998) and has strong
influences on behaviors that require persistence and effort. The strength of an individual’s belief
in their capability to perform a behavior also determines whether they form a behavioral
intention to do so. Consequently, perceived behavioral control not only has a direct effect on
behavior through its influence on persistence displayed during the execution of behaviors, but
also indirectly via its effects on the formation of behavioral intentions. Like attitudes and
subjective norms, global perceptions of control are related to specific beliefs, including beliefs
about resources and obstacles. For example, one may believe that one has a resource and that this
resource in turn facilitates a target behavior. Both of those conditions should increase perceived
behavioral control. Thus, beliefs contribute to perceived behavioral control, which thereby
facilitates or inhibits behavioral performance.
Since its inception, the TPB has been used to predict a variety of behaviors (for reviews
see Albarracin et al., 2001, 2004; Armitage & Conner, 2001; Godin & Kok, 1996; McEachan,
Conner, Taylor & Lawton, 2011). For example, several studies have applied the TPB in
predicting – and in some cases, promoting – health-related behaviors, including diet (Omondi,
Walingo, Mbagaya & Othuon, 2011), (un)healthy food intake (Fila & Smith, 2006; Guillaumie,
Godin, & Vézina-Im, 2010), binge drinking (Ross & Jackson, 2013), and condom use
(Albarracin et al., 2001, 2004). This model has also been applied to explain such disparate
behaviors as safe driving (Conner et al., 2007), mobile learning readiness (Cheon, Lee, Crooks &
Song, 2012), and environmentally friendly behaviors (Han, Hsu & Sheu, 2010). This abundance
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of research illustrates the importance and applicability of this model in contemporary research
and practice.
A large number of meta-analyses that have been conducted on both the TRA and TPB, to
explain the relation between intentions and behaviors across different contexts (e.g., Albarracίn
et al., 2001; Webb & Sheeran, 2006). Meta-analytic reviews of this literature have found that the
average correlation between intention and behavior is r = .45 (Albarracίn et al., 2001) and r = .57
(Webb & Sheeran, 2006). This association has been shown to decrease when the intention is
measured long before the behavior is performed (Albarracίn et al., 2004; Webb & Sheeran, 2006)
and when the behavior is habitual in nature (Webb & Sheeran, 2006). These models thus appear
to be excellent at predicting the association between attitudes, intentions and behavior.
The Model of Goal-Directed Behavior
The TRA and TPB have since evolved to include additional constructs (e.g., Shaw &
Shiu, 2013), as well as provide the foundation for other models. One such model is the model of
goal-directed behavior (MGDB; Perugini & Bagozzi, 2001). This model posits that desires are
the proximal antecedents of intentions, and that the antecedents described in the TPB act through
desires. Additionally, the model describes how consequences of behavior can be entered into the
model as anticipated emotions, which serve as determinants of desires, alongside attitudes,
subjective norms, and perceived behavioral control. Finally, the model highlights the importance
of frequency and recency of information regarding past behaviors in influencing behavioral
enactments directly and indirectly via intentions.
The TRA and TPB provide reasons for what prompts individuals to engage in a behavior,
but, according to Perugini and Bagozzi (2001), they do not include a motivational component
needed to describe what induces the generation of a behavioral intention. Desires, in this case,
53

The Psychology of Attitudes and Persuasion
provide this motivational component that trigger intentions, making them a conduit through
which attitudes, subjective norms, and perceived behavioral control work through. When these
antecedents provide a reason for performing the behavior, it produces a desire to act that
motivates an individual to form the intention to do so. Desires thus helps explain how the
existing predictors influence intentions.
By incorporating anticipated emotions as an antecedent, this model also takes into
account the fact that individuals use the emotional consequences tied to performing a behavior as
another criteria in their decision to enact, thereby making it a factor influencing their behavioral
intentions. Although this construct shares some overlap with attitudes, there are some
dissimilarities. For example, attitudes toward behaviors are a function of learning, and once
learned, are triggered automatically when exposed to the attitude object. Anticipated emotions,
however, are more dynamic, and are a result of the appraisal of the consequences associated with
performing a behavior. Therefore, they are more contingent on a particular instance and change
depending on the context.
Perugini and Bagozzi (2001) tested their model and found that desires are strong
predictors of intention formation, and thus important motivators in decision-making. The
researchers also found that their model accounted for more variance in intentions, compared to
the TPB. A recent study on international travel intentions lends further support to this conclusion.
By comparing the TRA, TPB, and the MGDB, the researchers found that the MGDB was
superior in its predictive validity, accounting for significantly more variance in travel intentions,
compared to the either the TRA or TPB (variance accounted for by the TRA, TPB and MGDB:
53.4%, 57.3%, and 79.3% respectively; Lee, Song, Bendle, Kim & Han, 2012). Thus, the model
54

The Psychology of Attitudes and Persuasion
of goal-directed behavior further broadens our understanding of the associations between
attitudes, intentions and behavior.
Ouellette and Wood (1998) conducted a meta-analysis to find a robust effect of past
behaviors on both intentions and future behaviors. This model takes this into account and posits
that, for frequently enacted behaviors, past behaviors reflect habit strength that has a direct effect
on future behaviors. Conversely, for behaviors that are not well learned, the frequency of past
behaviors has an indirect influence via intentions, with more frequently enacted behaviors
generating more favorable intentions.
Closing Note
We have had a rather lengthy discussion of issues that concern the psychology of
attitudes, including classic and contemporary notions. The field is vital and relevant to many
applications, from health to marketing to politics, and thus is likely to inspire many generations
of social and personality psychologists in the decades to come.
55

The Psychology of Attitudes and Persuasion
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Figure Caption
Figure 1. Google searches for attitudes over time.
Figure 2. The relation of attitudes with beliefs, intentions, behaviors and goals.
Figure 3. Dimensions of valence and arousal.
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