Persuasion as Belief Based Behavior Change Discussion Paper

CHAPTER 8Reasoned Action Theory
Persuasion as Belief-Based Behavior Change
Marco Yzer
Introduction
Almost 50 years after its inception, reasoned
action theory continues to serve as a foundation
for persuasion research. The popularity of the
theory lies in its direct applicability to the question of how exposure to persuasive information
leads to behavior change. Despite its wide use
and long history, reasoned action is a dynamic
theory with a number of unresolved issues. As
this chapter will show, some of these issues reflect
misconceptions of theoretical propositions or
misuse of research recommendations, whereas
others indicate opportunities for theoretical
advancement.
Reasoned action theory explains behavior by
identifying the primary determinants of behavior and the sources of these determinant variables, and by organizing the relations between
these variables. The theory is marked by a
sequence of reformulations that build on one
another in a developmental fashion. These are
the theory of reasoned action (Fishbein & Ajzen,
1975), the theory of planned behavior (Ajzen,
1985), and the integrative model of behavioral
prediction (Fishbein, 2000). The theory’s current
formulation, graphically displayed in Figure 8.1.,
is described as the reasoned action approach to
explaining and changing behavior (Fishbein &
Ajzen, 2010). In this chapter I use the term reasoned action theory to refer to the current formulation of the theory and to propositions that
apply to all formulations of the theory.
The objectives of this chapter are to make
clear how reasoned action theory contributes to
a better understanding of persuasion processes
and outcomes, and to identify accomplishments
of and opportunities for research in the reasoned
action tradition. Because of its relevance for persuasion scholarship, I will first highlight the reasoned action hypothesis that behavior change
originates from beliefs about the behavior. Next
I will discuss key propositions within the historical context in which they were developed, issues
related to conceptualization and operationalization of the theory’s components, and opportunities for future research. The range of issues
included in this review addresses the decadeslong time frame during which persuasion scholars have explicitly used core reasoned action
120
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Chapter 8. Reasoned Action Theory——121
Figure 8.1  Components of Reasoned Action Theory and Their Relations
Background
factors.
Examples:
Demographics
SES variables
Behavioral
beliefs and
evaluations
Attitude toward
behavior
• instrumental
• experiential
Normative
beliefs and
motivation to
comply
Perceived norm
• injunctive
• descriptive
Attitude toward
target
Culture
Religion
Persuasive
messages (e.g.,
media,
interpersonal)
Personality
Values
Control beliefs
and facilitating
power
Intention
Perceived
behavioral control
• capacity
• autonomy
Behavior
Actual control
• skills
• environmental
constraints
Knowledge
Identity
concepts. The research I review here is illustrative
rather than exhaustive, by necessity, as few other
behavioral theories have generated more research.
The Reasoned Action
Perspective on Persuasion
Beliefs that people hold about a behavior play a
central role in reasoned action explanations of
behavior. In Fishbein and Ajzen’s (2010) words,
“human social behavior follows reasonably and
often spontaneously from the information or
beliefs people possess about the behavior under
consideration. These beliefs originate in a variety
of sources, such as personal experience, formal
education, radio, newspapers, TV, the Internet
and other media, and interactions with family
and friends. . . . No matter how beliefs associated
with a given behavior are acquired, they serve to
guide the decision to perform or not perform the
behavior in question” (p. 20).
When people act on beliefs that they have
formed about a behavior, they engage in a reasoned, but not necessarily rational process. For
example, someone suffering from paranoid personality disorder may lock the door of his office
because he believes that his colleagues are conspiring against him. This person acts in a reasoned
manner on a belief, even though others would
deem his belief irrational. Regardless whether
beliefs are irrational, incorrect (because based on
false information), or motivationally biased, once
beliefs are formed they are the cognitive basis from
which behavior reasonably follows (Blank &
Hennessy, 2012; Fishbein & Ajzen, 2010).
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122——PART II. Theories, Perspectives, and Traditions
Beliefs affect behavior through a sequence of
effects. Specific beliefs about a behavior inform
attitude, perceived norm, and perceived behavioral control regarding the behavior, which in
turn determine intention to perform the behavior. If one has the necessary abilities to perform
the behavior and if there are no situational
obstacles that impede behavioral performance,
then intention should lead to behavior. The conceptualization of behavior formation as a process
makes clear that a persuasive message cannot
directly change behavior. Although the ultimate
objective of persuasive messages is to reinforce or
change a particular behavior, persuasive messages at best create or change beliefs. When
beliefs are appropriately selected, changes in
those beliefs should affect attitude, perceived
norm, or perceived behavioral control, which in
turn should affect intention and behavior. Those
beliefs that most strongly discriminate between
people who do and do not (intend to) perform a
particular behavior, are the choice candidates to
address in persuasive messages (Fishbein &
Ajzen, 2010; Fishbein & Yzer, 2003).
In terms of reasoned action theory, persuasion thus concerns the effects of exposure to a
persuasive message on beliefs about performing a
behavior, and through effects on those beliefs on
behavior. Clearly, then, the precision with which
one can predict behavior is directly relevant for
persuasion scholarship. The remainder of this
chapter will therefore be used to review the ability of reasoned action theory to predict behavior.
For this purpose it is useful to first discuss the
historical context in which reasoned action theory was developed.
Historical Context
In the early 20th century there was widespread
consensus that attitude should matter as a basis
for human behavior. For example, most contemporary definitions emphasized attitude as a tendency to act (for an overview see Allport, 1935).
By the 1960s, however, accumulated empirical
support for the hypothesis that people act on
their attitude was inconsistent at best, with many
studies reporting no effect of attitude on behavior at all. As a result, many scholars questioned
the usefulness of attitude for behavioral prediction. Most widely cited in this regard is Wicker
(1969), who, on a review of studies that correlated self-reported attitude with lagged observations of behavior, concluded that it is unlikely
that people act on their attitude. In counterpoint,
others argued that measurement issues were at
least in part responsible for weak correlations
between attitude and behavioral data. Particularly pertinent is Triandis’s (1964) finding that
the prediction of behavior from attitude
improved when measures of attitude and behavior represented the same dimensions.
The debate on the question whether attitude
predicts behavior helps understand the origins of
reasoned action propositions. In effect, what was
under discussion was whether contemporary
attitude theory offered valid hypotheses about
how thoughts, feelings, and behavior regarding
an object are associated. Fishbein observed that
the confusion surrounding the attitude-behavior
relation had to do with the wide range of different variables that were included under the
umbrella label of “attitude.” Similar to Thurstone
(1928), Fishbein (1967) viewed attitude as “a
relatively simple unidimensional concept, referring to the amount of affect for or against a psychosocial object” (p. 478). Building on Dulany’s
(1968) theory of propositional control over verbal responses, he argued that attitude should be
separated from its antecedents and consequences.
Moreover, in order to improve prediction of
behavior, he urged scholars to focus on the relations between these variables, that is, beliefs,
attitude, behavioral intention, and behavior
(Fishbein, 1963, 1967).
A number of principles have been developed
to aid such inquiry (e.g., Ajzen & Fishbein, 1973).
A first holds that prediction of behavior (e.g.,
running) is more precise than prediction of
behavioral categories (e.g., exercise) or goals (e.g.,
losing weight). Exercise includes many different
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Chapter 8. Reasoned Action Theory——123
behaviors, and each of these behaviors may be
associated with quite different beliefs. From the
author’s perspective, for example, running is fun
but swimming is not. Whether or not I will report
to like and engage in exercise therefore depends
on whether I think about running, swimming, or
both when asked about my exercise. Similarly, losing weight is a goal that can be achieved by many
different behaviors, and one may hold positive
beliefs about losing weight yet in fact not achieve
that goal because necessary dieting and exercise
behaviors are not performed due to negative
beliefs about those behaviors.
Second, prediction of specific behaviors is
more precise than prediction of general behaviors. Levels of specificity vary by the extent to
which a behavioral definition includes each of
four components, that is, action (e.g., running),
target (e.g., at a 9-minute per mile pace), context
(e.g., on a treadmill at the YMCA), and time (e.g.,
twice a week). Clearly, “running” can be interpreted more broadly than “running twice a week
at a 9-minute pace on a treadmill at the YMCA.”
When two people think about “running,” they
may therefore think about quite different behaviors, each associated with different, behaviorspecific beliefs. It is for this reason that persuasive
messages are more effective when they promote a
specific behavior and its underlying beliefs than
a general, more broadly interpretable behavior
(Fishbein, 2000).
Third, and known as the compatibility principle, prediction of behavior improves when
behavior is measured at the same level of specificity as beliefs, attitude, and intention (cf. Triandis,
1964). For example, intention to recycle hazardous materials may not correlate with frequency
of recycling batteries, because people may intend
to perform the more general behavior of recycling hazardous materials but not intend to perform the specific behavior of recycling batteries.
Adherence to these principles should improve
the precision of behavioral prediction, and consequently, the effectiveness of persuasive efforts.
Remarkably, however, although these principles
are as relevant for the prediction of behavior
today as when they were first introduced, they
continue to be violated in research that applies
reasoned action theory (Hale, Householder, &
Greene, 2002; Trafimow, 2004). This has important implications. For example, it has been shown
that measurement in accordance with the compatibility principle strengthens relations among
reasoned action variables, which suggests that
studies that do not adhere to this principle
underestimate the ability of reasoned action variables to explain intention and behavior (Cooke &
Sheeran, 2004; van den Putte, 1993).
Key Components
and Their Relations
Reasoned action theory has three structural parts
that together explain behavior formation: (a) the
prediction of behavior from behavioral intention; (b) the explanation of intention as a function of attitude, perceived norm, perceived
behavioral control, and their underlying beliefs;
and (c) the exposition of beliefs as originating
from a multitude of potential sources. I will use
this partition to structure a discussion of issues
related to each reasoned action component and
the proposed relations between components.
Behavior
The precision with which behavior can be
predicted improves when specific behaviors
rather than behavioral categories or goals are
measured, and when the behavior that one
wants to predict is measured at the same level of
specificity as the variables that are used to predict it. Another noteworthy measurement issue
has to do with the question whether behavior
should be observed or assessed with self-report
measures.
Whereas for pragmatic reasons most reasoned
action research uses self-reports of behavior,
observed behavior has an intuitive appeal because
it does not, or at least to a lesser extent, suffer
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124——PART II. Theories, Perspectives, and Traditions
from validity issues known to affect self-reports
of behavior (Albarracín et al., 2001). Key among
those is that self-reports of behavior can be exaggerated (e.g., male’s reports of sexual activity;
Brown & Sinclair, 1999) or understated (e.g.,
reports of at-risk health behavior; Newell, Girgis,
Sanson-Fisher, & Savolainen, 1999). Regardless
of whether these biases are deliberate or reflect
fallible cognitive estimation processes (Brown &
Sinclair, 1999), they render behavioral selfreports less than perfectly accurate. This does not
mean that prediction of observed behavior is
always more precise than prediction of selfreported behavior.
Consider, for example, Armitage’s (2005)
study of physical activity among members of a
gym. Armitage measured attitude, perceived
norm, perceived control, and intention at baseline with items framed in terms of “participating
in regular physical activity.” At a three-month
follow-up he assessed behavior by both asking
gym members enrolled in his study “How often
have you participated in regular physical activity
in the last 3 months?” and by electronically logging gym entrance. Clearly, baseline measures
were more compatible with the self-report
behavior measure than with the observed behavior measure. As just one example, when people
think about regular physical exercise, they may
think about activities outside the gym that are
not reflected in records of gym attendance, but
that likely are reflected in self-reports of physical
exercise. In support of this contention Armitage
found a stronger correlation of intention to participate in regular physical exercise with selfreported regular physical exercise, r = .51, than
with records of gym attendance, r = .42. This
finding has been corroborated in meta-analytic
research (Armitage & Conner, 2001; but see
Webb & Sheeran, 2006).
A moment’s reflection shows that the attitude,
perceived norm, perceived control, and intention
measures that Armitage used would have been
more compatible with, and thus more predictive
of, the self-report behavior measure used three
months after baseline if the former would have
asked about “participating in regular physical
activity in the next three months.” This is an issue
that affects many prospective studies. Interestingly, however, discussions about improving
behavioral prediction predominantly focus on
variables that possibly moderate effects of reasoned action variables on self-reported behavior,
and remain largely silent on measurement of
behavior itself (for a notable exception, see Falk,
Berkman, Whalen, & Lieberman, 2011). To be
sure, moderator analysis has important potential
for determining when the theory’s propositions
are particularly likely to apply, which not only
directs investigators to appropriate application
but also suggests areas for further theory development (Weinstein & Rothman, 2005). Even so,
the scarcity of work that tests the validity of
self-report behavior measures, for example, by
assessing compatibility between behavioral
determinant and behavior measures, is striking
(Albarracín et al., 2001).
Behavioral Intention
Behavioral intention is the most immediate
determinant of behavior. It is defined as people’s
readiness to perform a behavior: “Intentions are
assumed to capture the motivational factors that
influence a behavior; they are indications of how
hard people are willing to try, of how much of an
effort they are planning to exert, in order to perform the behavior” (Ajzen, 1985, p. 181). Intention is indicated by the subjective probability of
behavioral performance, that is, by people’s estimate of how likely it is that they will or will not
perform a particular behavior. Examples of
widely used intention items are How likely is it
that you . . . (followed by the definition of the
behavior under investigation; scale anchors
I definitely will not—I definitely will) and I intend
to . . . (scale anchors I completely disagree—
I completely agree).
The intention concept and its operationalization have not been universally accepted, however.
Concerned about the sufficiency of intention as
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Chapter 8. Reasoned Action Theory——125
the only variable that directly determines behavior,
investigators have proposed several alternative
intention concepts and measures. This section
reviews three such measures.
Warshaw and Davis (1985) proposed that
behavioral expectations, or people’s selfpredictions regarding their behavior, are superior
to behavioral intention in predicting behavior,
because behavioral expectations take possible
barriers to behavioral performance into account
more so than intention. Items such as I expect
to . . . and I will . . . (scale anchors highly unlikely
to highly likely) are commonly used to measure
behavioral expectation. Empirical findings suggest that behavioral expectation measures do not
outperform intention measures (Armitage &
Conner, 2001; Fishbein & Stasson, 1990; Sheeran
& Orbell, 1998; but see Sheppard, Hartwick, &
Warshaw, 1988), and it is not uncommon to
combine the two types of measures into a single
intention scale (e.g., Fielding, McDonald, &
Louis, 2008).
Gibbons, Gerrard, Blanton, and Russell (1998)
proposed behavioral willingness as another alternative for intention. Gibbons and colleagues
argued that an intention to act implies rational
deliberation, whereas behavior often is irrational
and triggered by situational factors. Developed in
the context of health-risky behavior, the behavioral willingness hypothesis holds that people
may intend to engage in safe behavior, but be
willing to engage in risky behavior if the situation would offer opportunities for doing so. For
example, someone may intend to have no more
than three drinks at a party, but drink more when
at the party an attractive person offers a fourth
drink. Similar to this example, behavioral willingness measures ask whether people would be
willing to engage in a particular behavior given a
particular scenario, that is, under specified circumstances. It is therefore unclear whether
behavioral willingness is truly different from
intention or simply a more specific intention
(Fishbein & Ajzen, 2010).
Gollwitzer’s (1999) concept of implementation intentions offers a greater contribution to
behavioral prediction. Implementation intentions are highly specific plans people make about
when, where, and how to act on a motivation to
act, that is, on their intention to act. There is
evidence that implementation intentions improve
the prediction of behavior (e.g., Ziegelmann,
Luszczynska, Lippke, & Schwarzer, 2007), but not
always (e.g., Budden & Sagarin, 2007; for a
review, see Gollwitzer & Sheeran, 2006). Instead
of a viable alternative to the intention variable,
implementation intentions are perhaps better
interpreted as a useful moderator, such that
people who formed positive intentions are more
likely to act on their intentions if they have also
thought about how to implement their plans.
Predicting Behavior From Intention
Reasoned action theory has been able to
account for behavior with a good measure of
success. For example, meta-analyses of studies
that prospectively examined behavior found
intention-behavior correlations to average around
r = .45 (e.g., Albarracín et al., 2001; Armitage &
Conner, 2001; Cooke & Sheeran, 2004; Hagger,
Chatzisarantis, & Biddle, 2002; Sheeran & Orbell,
1998; Sheppard et al., 1998).Whereas these average correlations usefully indicate the theory’s
general ability to account for behavior, it is
important to understand which factors increase
or decrease the strength of association between
intention and behavior. Before discussing two
such factors, I first address an important methodological implication of the hypothesis that intention predicts behavior.
Testing Prediction
To test the hypothesis that intention predicts
behavior, behavior should be measured some time
after the variables that theoretically predict it were
measured. Because behavior assessed at a certain
time point indicates what people did at that same
time (for observed behavior) or have done prior to
that time (for self-reported behavior), correlating
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126——PART II. Theories, Perspectives, and Traditions
cross-sectional intention and behavior data
produces a causal inference problem (Huebner,
Neilands, Rebchook, & Kegeles, 2011; Webb &
Sheeran, 2006; Weinstein, 2007). A cross-sectional
intention-behavior correlation indicates the extent
to which intention is consistent with people’s past
behavior, and should not be interpreted as prediction of future behavior. Unfortunately, inten­tionbehavior correlations obtained from cross-sectional
designs are still being published as tests of behavioral prediction (e.g., de Bruijn, Kremers, Schaalma,
Van Mechelen, & Brug, 2005; Keats, Culos-Reed,
Courneya, & McBride, 2007; Kiviniemi, VossHumke, & Seifert, 2007).
Lagged measurement is challenging, both for
methodological and budgetary reasons. It is therefore not surprising that cross-sectional studies
greatly outnumber prospective studies. For example, Albarracín and colleagues (2001) collected 96
samples for their meta-analysis, but of these, only 23
could be used to test the theory’s ability to predict
behavior. Similarly, Armitage and Conner (2001)
obtained correlations from 185 samples, yet only
44 of these provided lagged intention-behavior
correlations, and of the 33 samples that Cooke and
French (2008) analyzed, 19 could be used to
test intention effects on behavior (but see Hagger
et al., 2002, for a higher ratio). This means that
although reasoned action theory was designed to
predict behavior, it is primarily used to explain
intention. This gives pause for reflection: Despite
the thousands of reasoned action studies now in
existence, only a fraction provides a convincing
test of this key aspect of the theory.
Moderators of Intention
Effects on Behavior
At least two factors determine the strength of
intention-behavior relations. To begin, intention
should affect behavior to the extent that intention
is temporally stable. If between assessments of
intention and behavior nothing happens that
might change someone’s intention, then intention data should predict behavioral data. However, if intention changes between assessments
because, for example, someone is exposed to a
persuasive message, then the behavior data reflect
an intention formed after intention data were
obtained. The longer the gap between assessments of intention and behavior, the more likely
it is that intention changes, thereby attenuating
the intention-behavior correlation. Sheeran and
colleagues (Sheeran & Orbell, 1998; Sheeran,
Orbell, & Trafimow, 1999) found empirical support for this idea. For example, in a meta-analysis
of 28 prospective condom use studies, Sheeran
and Orbell (1998) found that intention-behavior
relations were stronger when the time between
measurement of intention and behavior was
short rather than long. Note, however, that there
is no gold standard for the optimal time lag
between intention and behavior assessments, in
part because it is near impossible to predict when
people will be exposed to factors that influence
their intention.
The relation between intention and behavior is
also conditional on actual control over behavioral
performance (Ajzen, 1985; Fishbein & Ajzen,
2010). People are thought to have actual control
over behavioral performance when they have the
necessary skills and when the situation does not
impose constraints on behavioral performance.
Thus, when despite positive intentions people do
not perform a behavior, behavioral nonper­
formance is not a motivational problem but a
problem of competence (i.e., deficient skills or
abilities) and means (i.e., presence of environmental constraints). It is here where the aforementioned implementation intentions prove useful;
actual behavior is more likely when people plan
how and when to act on their intention (Norman
& Conner, 2005; van Osch et al., 2009), possibly
because planning requires people to consider the
skills it takes and the obstacles they are up against
when they would perform a particular behavior.
Attitude and Behavioral Beliefs
Attitude is an evaluation of performing a
future behavior in terms of “favor or disfavor,
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Chapter 8. Reasoned Action Theory——127
good or bad, like or dislike” (Fishbein & Ajzen,
2010, p. 78). Although attitude is typically analyzed with a single composite scale, attitude is
thought to have two aspects, namely an instrumental (or cognitive) aspect, indicated by perceptions of, for example, how foolish or wise,
useful or useless performing a behavior is, and an
experiential (or affective) aspect, indicated by
how unpleasant or pleasant, unenjoyable or
enjoyable performing the behavior is perceived
to be. The relative importance of instrumental
and experiential aspects of attitude as determinants of intention have clear implications for
persuasive messages; if instrumental attitude
matters most, a message should emphasize the
usefulness of the recommended behavior, but if
experiential attitude is more important, a message should emphasize how enjoyable the behavior is. Unfortunately, however, because published
reports often do not make clear whether attitude
was measured with instrumental, experiential, or
both types of items, inferences about when
instrumental and experiential attitude contribute
to behavioral prediction cannot be made with
full confidence. The question whether differential impact is predictable thus deserves more
systematic inquiry than it has received thus far.
According to reasoned action theory, attitude
formation is the process by which a potentially
large set of specific beliefs, which has associated
with a behavior over time, informs an overall
sense of favorableness toward the behavior. Consistent with expectancy-value perspectives, attitude is a multiplicative combination of behavioral
beliefs, which are perceptions of the likelihood
that performing a particular behavior will have
certain consequences, and an evaluation of those
consequences in terms of good or bad. For example, two persons may both believe that if they use
a tanning bed, they will get a tan. In addition,
person A thinks that being tanned is good, but
person B does not. In this single belief example,
both person A and person B think that using a
tanning bed will give them a tan, but because
their opposite evaluations of being tanned person
A’s attitude toward using a tanning bed is positive
and person B’s attitude is negative. This makes
clear that both beliefs about behavioral consequences and evaluations of those consequences
need to be considered to determine favorableness
toward a behavior. It also makes clear that to
change attitude, persuasive messages can address
beliefs about the likelihood of particular consequences of a behavior but also address evaluations of those consequences. For example, suppose
that people already believe that unprotected sex
may lead to gonorrhea but do not evaluate gonorrhea as a very serious disease. In this case, a message does not need to argue that unprotected sex
can lead to gonorrhea, but can improve attitude
toward using condoms if a message convinces
that gonorrhea is quite serious.
Although belief-evaluation product terms
have been found to correlate strongly with attitude (Albarracín et al., 2001), they typically do
not explain much more variance in attitude than
the separate behavioral beliefs (e.g., Armitage,
Conner, Loach, & Willetts, 1999). For this reason,
most investigators only assess behavioral beliefs,
or the perceived likelihood of behavioral consequences. Note, however, that for statistical reasons product terms are unlikely to be associated
with large effects in regression analysis, which is
the method commonly used to test reasoned
action (Ajzen & Fishbein, 2008; Yzer, 2007). We
should be careful not to abandon conceptual
ideas on the basis of empirical results if those
results reflect statistical artifacts.
Perceived Norm and
Normative Beliefs
To capture the influence of people’s social
environment on their intention to perform a
particular behavior, Fishbein and Ajzen (1975;
Ajzen & Fishbein, 1973; Fishbein, 1967) proposed the concept of subjective norm as a second determinant of behavioral intention. In the
theory of reasoned action (Fishbein & Ajzen,
1975) subjective norm is the extent to which
I believe that other people think that I should or
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128——PART II. Theories, Perspectives, and Traditions
should not engage in a particular behavior.
Other scholars refer to subjective norm as
injunctive norm (Cialdini, Reno, & Kallgren,
1990), and in recent years, reasoned action theorists have used “injunctive norm” rather than
“subjective norm” to indicate expected approval
or disapproval from others (Fishbein, 2000;
Fishbein & Ajzen, 2010).
The question whether subjective norm is able
to capture all relevant perceived social influence
has been controversial. This question in large
part stemmed from empirical findings in which
subjective norm contributed little to the explanation of intention (Albarracín et al., 2001; Cooke
& French, 2008; Hagger et al., 2002). Note, however, that there is evidence that subjective norm
matters in collectivistic populations (Giles,
Liddell, & Bydawell, 2005; Lee & Green, 1991),
in younger samples (Albarracín, Kumkale, &
Johnson, 2004; van den Putte, 1993), and for
behaviors that have salient social aspects (Cooke
& French, 2008; Finlay, Trafimow, & Moroi,
1999), which implies that normative messages
can have strong persuasive potential for some
identified segments and behaviors. Even so,
because much work found relatively small subjective norm effects, many investigators have
tested alternative normative measures, including,
among others, personal norm, verbal approval,
social support, and descriptive norm (e.g., Larimer,
Turner, Mallett, & Geisner, 2004; van den Putte,
Yzer, & Brunsting, 2005).
In recognition of a need to expand the scope
of the normative component, reasoned action
theory currently posits a perceived norm component that is the composite of injunctive and
descriptive norms (see also Fishbein, 2000). The
descriptive norm indicates the extent to which
I believe that other people perform a particular
behavior themselves (Cialdini, Reno, & Kallgren,
1990). A meta-analysis of 14 correlations showed
that descriptive norms explained variance in
behavioral intention that subjective norms did
not, supporting the discriminant validity of the
descriptive norm variable (Rivis & Sheeran,
2003). In addition, injunctive and descriptive
norms can have differential effects (Larimer
et al., 2004), not only in magnitude but also in
direction (Jacobson, Mortensen, & Cialdini,
2011). Thus, although in the context of reasoned
action theory, injunctive and descriptive norms
can be analyzed with a composite perceived
norm scale, it may prove useful to also examine
the effects of these variables separately.
Injunctive and descriptive norm measures tap
normative perceptions regarding “most people
who are important to me.” Perceived norm thus
reflects perceived social pressure to perform or
not to perform a behavior that is generalized
across specific referents. It is a function of beliefs
about particular individuals; whether particular
individuals think I should perform a behavior
(injunctive normative beliefs) or whether those
individuals perform the behavior themselves
(descriptive normative beliefs). However, believing that a particular individual prescribes a certain behavior will not matter if one does not care
what that individual thinks, that is, if one is not
motivated to comply with that individual. For
example, someone affected by diabetes may
expect that her doctor will approve her injecting
insulin, but also believe that her friends will disapprove, or believe that her insulin-dependent
friends do not self-inject. If it is more important
for her to do what her peers want her to do than
what her doctor wants her to do, then she will
experience an overall sense of pressure against
injecting insulin.
In more general terms, perceived norm is a
function of normative beliefs about particular
individuals weighed by the extent to which
someone wants to comply with those individuals.
However, as discussed in the context of multiplicative composites of behavioral beliefs and their
evaluations, effects of product terms are hard to
demonstrate in regression analysis. Reasoned
action research often relies on regression analysis, which explains why there is not much evidence to support multiplicative composites of
normative beliefs and motivation to comply
(Fishbein & Ajzen, 2010). The usefulness of normative beliefs and motivation to comply should
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Chapter 8. Reasoned Action Theory——129
not be rejected if a lack of empirical support for
these measures is caused by a statistical artifact.
For example, Giles and colleagues (2005) examined both normative beliefs and motivation to
comply regarding condom use in a sample of
Zulu adults. Their analysis allowed them to identify important sources of influence, which in turn
could inform decisions about who to target in
behavior change interventions.
Perceived Behavioral Control
and Control Beliefs
Concerned that the theory of reasoned action’s
focus on volitional behavior unnecessarily
restricted the scope of the theory, Ajzen (1985)
argued that the theory could also predict nonvolitional behavior if it would address perceptions of control over behavioral performance.
His inclusion of a perceived behavioral control
variable as an additional determinant of intention and behavior established the theory of
planned behavior (Ajzen, 1985, 1991). Perceived
behavioral control was initially defined as
“. . . people’s perception of the ease or difficulty
of performing the behaviour of interest” (Ajzen,
1991, p. 183), and “compatible with . . . perceived
self-efficacy” (p. 184). Consistent with this definition, items widely used to measure perceived
behavioral control ask how much control people
believe they have over performing a behavior,
how easy or difficult they believe performing the
behavior will be, or how confident they are that
they can perform the behavior.
The proposed equivalence of perceived control,
perceived difficulty, and self-efficacy has been the
subject of considerable debate. Arguments in that
debate for the most part are based on empirical
tests of the dimensionality of perceived behavior
control. A common finding from such tests is that
confidence-framed items and control-framed
items load onto separate factors (e.g., Armitage &
Conner, 1999; Kraft, Rise, Sutton, & Røysamb,
2005). Importantly, these two factors are often
interpreted as indicating “perceived behavioral
control” and “self-efficacy,” suggesting a theoretical distinction between the two (Norman & Hoyle,
2004; Terry & O’Leary, 1995). Building on this
idea, investigators have used the two item clusters
to explore whether perceived behavioral control or
self-efficacy offers a better explanation of intention or behavior (e.g., Pertl et al., 2010; Rodgers,
Conner, & Murray, 2008).
The contention that perceived behavioral
control and self-efficacy are theoretically distinct
is unconvincing, however, if based solely on
empirical criteria (such as proportions of variance explained) and without careful consideration of what these concepts are supposed to
mean. For example, Terry and O’Leary (1995)
purported to contrast perceived control and selfefficacy, but only used easy-difficult items to
measure self-efficacy. It is not clear, however, why
easy-difficult items are best seen as self-efficacy.
Indeed, there is evidence that at least in some
behavioral domains, easy-difficult is more closely
related with attitude (Kraft et al., 2005; Yzer,
Hennessy, & Fishbein, 2004) or intention (Rhodes
& Courneya, 2003) than with control. Thus,
whereas control items often load on two separate
factors, this by itself does not irrefutably confirm
the conceptual separation of perceived control
and self-efficacy. Rhodes and Courneya (2003)
warn in this regard against backward theorizing:
“. . . items should be created to indicate theoretical concepts; theoretical concepts should not be
created to indicate items!” (p. 80).
Fishbein and Ajzen (2010) similarly observe
that “. . . although there is good empirical evidence
that items meant to assess perceived behavioral
control can be separated into two factors, identifying
them as self-efficacy expectations and perceived
control is misleading and unjustified” (p. 165).
They argue that self-efficacy (Bandura, 1997) and
perceived behavioral control are conceptually similar; both center on people’s perception of whether
they can carry out a particular behavior. Consistent
with this, reasoned action theory posits that perceived behavioral control/self-efficacy is a latent
variable that has two aspects, namely capacity and
autonomy. Capacity is indicated by items asking
(c) 2013 Sage Publications, Inc. All Rights Reserved.
130——PART II. Theories, Perspectives, and Traditions
people how certain they are that they can perform
a behavior. Autonomy is indicated by items asking
people how much they feel that performing a
behavior is up to them. Capacity and autonomy
can be congruent, but there are situations in which
they are not. For example, someone may believe
that the decision to climb a tall building is up to
him, but feel certain that he cannot do so because
he is afraid of heights. Depending on the purpose
of the investigation, capacity and autonomy thus
can be combined or analyzed separately. Similarly,
to enhance perceived behavioral control over a
behavior, persuasive messages can focus on skill
building, emphasize autonomous decision-making,
or do both. The appeal of a multiaspect interpretation of perceived behavioral control is that it clarifies its conceptual definition, and refocuses our
attention to the possibility of additive contributions of capacity and autonomy to behavioral prediction rather than superiority of one over the
other. It also is a new idea, and thus should be a
priority in future research.
The belief basis of perceived behavioral control
consists of control beliefs (i.e., the perceived likelihood of having particular resources and opportunities for behavioral performance) and perceived
power (i.e., the extent to which those resources
and opportunities facilitate or obstruct behavioral
performance). Perceived behavioral control is
proposed to be the sum of the control beliefsperceived power product terms. The belief basis of
perceived behavior control has received curiously
little research attention (see, e.g., Armitage &
Conner, 2001). Therefore, and also considering
the recent reconceptualization of perceived behavior control, systematic tests of control beliefs offer
good opportunities for theoretical advancement.
Explaining Intention
Reviews of studies on determinants of intention
have found multiple correlations in the R = .55-.70
range (e.g., Albarracín et al., 2001; Armitage &
Conner, 2001; Hagger et al., 2002; Rivis &
Sheeran, 2003; van den Putte, 1993). These results
are impressive, particularly considering that they
are based on studies that differ considerably in
inclusion and measurement of predictor variables. At the same time, it should be noted that
these multiple correlations reflect the effects of
direct measures of attitude, perceived norm, and/
or perceived behavioral control on intention.
Relatively few studies have examined the role of
beliefs in intention formation. Van den Putte
(1993), for example, reports that of the 150 independent samples he analyzed, only 18 measured
both behavioral beliefs and attitude, and only 13
measured both normative beliefs and subjective
norm. The curious neglect of beliefs is disconcerting, because beliefs are the basis of persuasive
messages that seek to change behavior.
A possible explanation for this phenomenon
is that because of the availability of attitude, perceived norm, perceived behavioral control, and
intention measure templates (e.g., Fishbein &
Ajzen, 2010), designing measures of these four
variables is a fairly straightforward affair. However, determining which beliefs are salient in a
particular population is not as straightforward:
“. . . although an investigator can sit in her or his
office and develop measures of attitudes, perceived norms and [perceived behavioral control],
she or he cannot tell you what a given population
(or a given person) believes about performing a
given behavior. Thus one must go to members of
that population to identify salient outcome, normative and [control] beliefs” (Fishbein, 2000,
p. 276). Recommendations for belief elicitation
procedures are also available, however, (Ajzen &
Fishbein, 1980; Fishbein & Ajzen, 2010), and
there thus is no good reason for disregarding
beliefs if one seeks to explain intention.
Background Factors and the
Question of Sufficiency
Beliefs originate from a large number of sources.
Interaction with other people, engagement with
(c) 2013 Sage Publications, Inc. All Rights Reserved.
Chapter 8. Reasoned Action Theory——131
media messages, growing up in a particular culture, membership of a religious community, and
even gender and personality, for example, can all
play a role in forming and shaping beliefs about a
particular behavior. In the language of reasoned
action theory, these variables are background factors, which are possibly but not necessarily related
with beliefs. Similarly, background factors do not
affect intention and behavior directly, but indirectly through beliefs. Thus, for example, if gender is empirically associated with intention or
behavior, gender also should be correlated with
beliefs, that is, men and women should hold different beliefs (Fishbein, 1967; Fishbein & Ajzen,
2010). Such findings can usefully inform decisions about which beliefs to target in different
gender segments.
The conceptualization of background factors
is directly relevant for a persistent debate on the
question whether reasoned action variables are
sufficient for explaining intention and behavior
(for review, see Fishbein & Ajzen, 2010, chapter 9).
Relevant for the present discussion of background factors is a substantial body of research
that proposed an extension of the theory to better account for intention. Specifically, a number
of different variables have been suggested as a
fourth determinant variable in addition to attitude, perceived norm, and perceived behavioral
control, including, among many others, gender,
self-identity, and culture. Such research efforts
are commendable to the extent that they promote theoretical development. However, many
recommendations for extending reasoned action
theory do not start from compelling conceptual
arguments, but instead rely on empirical markers
such as change in proportion of explained variance. The logic that if a particular variable
explains variance in intention, it must be an
important predictor has important statistical
problems (Trafimow, 2004). A correlation
between a particular variable and intention
therefore does not conclusively prove that the
variable is a predictor of intention and not a
background factor.
New Directions and
Opportunities for
Future Research
The thousands of reasoned action studies now in
existence address only a limited number of questions and use only a limited number of metho­
dologies. For example, studies that explain
intention far outnumber studies that prospectively examine behavior and studies that examine
beliefs; and studies that use survey methodology
far outnumber experimental studies. Although
survey-based tests of intention usefully show
whether in a particular population intention to
perform a particular behavior is guided by attitude, perceived norm or perceived behavioral
control, belief-based and behavioral analyses are
at least as interesting to persuasion scholars. In
addition, there are other questions that should
appear more prominently on research agendas
than they have thus far. Two of these have to do
with developing hypotheses about when reasoned action variables will predict which behaviors, and how reasoned action can inform
message design.
Predicting Prediction
Reasoned action theory proposes that to predict intention and behavior only a small number
of variables need to be considered. Because each
behavior is substantively unique, which of these
variables most critically guide a particular behavior in a particular population is an empirical
question. Clear research recommendations have
been developed for identifying those critical variables (e.g., Fishbein & Ajzen, 2010; Fishbein &
Yzer, 2003), and there is evidence that interventions that follow these recommendations can
effectively change behavior (e.g., Albarracín
et al., 2005).
Although the basic assumption of the uniqueness of each behavior is true in principle, the
implication that identification of a behavior’s
(c) 2013 Sage Publications, Inc. All Rights Reserved.
132——PART II. Theories, Perspectives, and Traditions
critical predictor is an empirical question is not
altogether satisfactory. Both for scholarly and
intervention purposes, it would be more advantageous if prediction could be predicted, that is,
if it would be possible to hypothesize which reasoned action variable will predict a particular
behavior in a particular population. There is
some evidence that this is a realistic objective.
For example, experimental work has corroborated behavior and population features that
determine the predictive power of perceived
norm (Jacobson, Mortensen, & Cialdini, 2011;
Trafimow & Fishbein, 1994).
One can turn to other theory to derive principles that can help understand when specific
reasoned action variables will explain behavior
(Fishbein & Ajzen, 2010; Weinstein & Rothman,
2005). For example, Lutchyn and Yzer (2011)
used construal level theory (Trope & Liberman,
2003) to test the implications of changing the
time component of behavioral definitions for the
relative importance of behavioral and control
beliefs. Construal level theory proposes that
people use abstract terms to construe behaviors
that are to be performed some time in the future.
Construals of such distant behaviors emphasize
the “why” aspects of behavior, and describe
behavior in terms of the value or desirability of
a behavioral outcome, or in reasoned action
terms, behavioral beliefs. In contrast, construals
of near future behaviors are more concrete and
represent the “how” aspect of the behavior. They
reflect feasibility of the behavior, or in reasoned
action terms, control beliefs. Lutchyn and Yzer
(2011) found that the salience of beliefs is a function of time frame, such that when the time
component in a behavioral definition moves
from the near to the distant future, the salience of
behavioral beliefs increases and the salience of
control beliefs decreases. These findings have
implications for message design. To motivate
distant behavior, messages need to address
behavioral consequences. For example, a message
sent in September to motivate people to get a flu
shot right before the flu season’s expected onset
in December can emphasize the benefits of getting a flu shot. To affect near future behavior, for
example, getting a flu shot this week, messages
should include references to control beliefs, for
example, information about where one can get
free flu shots.
Moving Beyond Message Content
Interventionists can use reasoned action theory to identify the behavioral, normative, and/or
control beliefs that guide people’s behavior. It is
these beliefs that messages should address. The
theory thus is a tool for informing message content. It was not designed to inform the next necessary question in the message design process;
which audiovisual, narrative, duration, and other
stylistic message features will change the beliefs
addressed in the message? Fishbein and Ajzen
(2010) commented thus on the boundaries of
reasoned action theory: “Selection of appropriate
primary beliefs is perhaps our theory’s most
important contribution to behavior change
interventions. The theory offers little guidance as
to the specific strategies that will most effectively
bring about the desired changes in behavioral,
normative, or control beliefs. Such guidance
must come from outside our theory” (p. 367).
Some guidance is available. The literature on
communication campaigns, for example, offers
excellent overviews of components and design
steps of successful campaigns (Rice & Atkin,
2009). Similarly, scholars have addressed the
complementary nature of behavior change and
message effects theories for the purpose of
improving cancer prevention (Cappella, 2006).
Such work highlights that message development
involves decisions about both content and creative design, and that different theories are to be
used to inform each of these decisions. Which
theories in particular complement reasoned
action theory is a relatively unexplored question,
but one that if answered can greatly advance
understanding of persuasive messages.
(c) 2013 Sage Publications, Inc. All Rights Reserved.
Chapter 8. Reasoned Action Theory——133
Conclusion
Seen through a reasoned action lens, persuasion
is belief-based behavior change. Therefore, the
better one understands which beliefs cause
behavior by what process, the better able one is to
design successful messages. The review presented
in this chapter discussed that if used correctly,
reasoned action theory can identify the beliefs
that explain why people do or do not perform a
particular behavior. It also identified a number of
issues that if addressed can deepen our understanding of behavioral prediction. Akin to how
reasoned action theory was first conceived, to
address these issues, an outward-looking strategy
that draws on complementary theory will generate greatest progress. The challenge for future
research is twofold; more precise predictions
about how and when reasoned action variables
predict intention and behavior are needed, and
in addition, message design strategies that can
change these variables need to be identified.
These are challenges that promise exciting
research, significant theoretical advancement,
and effective practical application.
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attitude objects. Journal of Social Issues, 25, 41–78.
Yzer, M. C. (2007). Does perceived control moderate
attitudinal and normative effects on intention? A
review of conceptual and methodological issues. In
I. Ajzen, D. Albarracin, & R. Hornik (Eds.), Prediction and change of health behavior: Applying the
reasoned action approach (pp. 107–123). Mahwah,
NJ: Erlbaum.
Yzer, M. C., Hennessy, M., & Fishbein, M. (2004). The
usefulness of perceived difficulty for health research.
Psychology, Health and Medicine, 9, 149–162.
Ziegelmann, J. P., Luszczynska, A., Lippke, S., &
Schwarzer, R. (2007). Are goal intentions or
implementation intentions better predictors of
health behavior? A longitudinal study in orthopedic rehabilitation. Rehabilitation Psychology,
52, 97–102.
(c) 2013 Sage Publications, Inc. All Rights Reserved.
[student name removed]
Item 4
Rhodes’ (2012) chapter on “Outcomes of Persuasion: Behavioral, Cognitive, and Social”
discusses the roles of behavior, attitudes, and norms in the persuasive process. Changes in
behavior are the keys to understanding the effectiveness of persuasive messages. However, they
are difficult to measure directly. One approach to address this problem is to look to proxiessuch
as aggregated behavior, behavioral intention, behavioral willingness, and self-report.
Meanwhile, another approach is to totally bypass behavior measurement and instead study
persuasion’s effect on attitudes and norms by analyzing accessibility and performing explicit or
implicit tests. Research shows that attitudes and norms that are more accessible will more greatly
influence how people perceive and understand social situations, and in turn, how they behave in
those situations– especially when spontaneous decision making is at play. The reason being,
according to Fazio’s process model, is that when people are not highly motivated and do not
have sufficient opportunity to carefully consider all of the available information, they will look
to attitudes and norms that are quickly activated in their minds. Hence, accessibility canbe
measured by testing reaction times. Furthermore, research demonstrates that implicit attitude
measures are mostly predictive of deliberative behaviors, while explicit attitude measures are
more likely to predict spontaneous behaviors.
Overall, understanding how to measure the different outcomes of persuasion is useful in
informing persuaders of how to better assess the effectiveness of their persuasive messages
depending on their goals. That way, if they find that they are successful, they can continue doing
what they’re doing. Or, if they find that they are lacking in a particular area, they can know to
pivot and use a different approach. For example, if the goal of the persuasive message is to
encourage individuals to floss their teeth at least once a day, and everyday routines are likely to
involve spontaneous rather than deliberative decisions, it would be best to use implicit
measures of attitudes or test reaction times to measure success. Ultimately, as a person is
exposed to persuasive information about flossing, the accessibility of their attitudes and norms
and any resulting behavior are not only outcomes for each encounter, but also serve as inputs
the next time that persuasive information comes up again in their environment. It’s a
never-ending cycle of exposure, attention, action, and reinforcement.
Rhodes mentions that the influence of norms has only been explicitly tested in Fazio’s
process model. How might we go about implicitly testing norms? What implications might
thishave for understanding the different outcomes/inputs of persuasion? Also, I’d be curious
to know how persuaders factor norms and attitudes into their audience analysis.
CHAPTER 8
Reasoned Action Theory
Persuasion as Belief-Based Behavior Change
Marco Yzer
Introduction
Almost 50 years after its inception, reasoned
action theory continues to serve as a foundation
for persuasion research. The popularity of the
theory lies in its direct applicability to the question of how exposure to persuasive information
leads to behavior change. Despite its wide use
and long history, reasoned action is a dynamic
theory with a number of unresolved issues. As
this chapter will show, some of these issues reflect
misconceptions of theoretical propositions or
misuse of research recommendations, whereas
others indicate opportunities for theoretical
advancement.
Reasoned action theory explains behavior by
identifying the primary determinants of behavior and the sources of these determinant variables, and by organizing the relations between
these variables. The theory is marked by a
sequence of reformulations that build on one
another in a developmental fashion. These are
the theory of reasoned action (Fishbein & Ajzen,
1975), the theory of planned behavior (Ajzen,
1985), and the integrative model of behavioral
prediction (Fishbein, 2000). The theory’s current
formulation, graphically displayed in Figure 8.1.,
is described as the reasoned action approach to
explaining and changing behavior (Fishbein &
Ajzen, 2010). In this chapter I use the term reasoned action theory to refer to the current formulation of the theory and to propositions that
apply to all formulations of the theory.
The objectives of this chapter are to make
clear how reasoned action theory contributes to
a better understanding of persuasion processes
and outcomes, and to identify accomplishments
of and opportunities for research in the reasoned
action tradition. Because of its relevance for persuasion scholarship, I will first highlight the reasoned action hypothesis that behavior change
originates from beliefs about the behavior. Next
I will discuss key propositions within the historical context in which they were developed, issues
related to conceptualization and operationalization of the theory’s components, and opportunities for future research. The range of issues
included in this review addresses the decadeslong time frame during which persuasion scholars have explicitly used core reasoned action
120
(c) 2013 Sage Publications, Inc. All Rights Reserved.
Chapter 8. Reasoned Action Theory——121
Figure 8.1  Components of Reasoned Action Theory and Their Relations
Background
factors.
Examples:
Demographics
SES variables
Behavioral
beliefs and
evaluations
Attitude toward
behavior
• instrumental
• experiential
Normative
beliefs and
motivation to
comply
Perceived norm
• injunctive
• descriptive
Attitude toward
target
Culture
Religion
Persuasive
messages (e.g.,
media,
interpersonal)
Personality
Values
Control beliefs
and facilitating
power
Intention
Perceived
behavioral control
• capacity
• autonomy
Behavior
Actual control
• skills
• environmental
constraints
Knowledge
Identity
concepts. The research I review here is illustrative
rather than exhaustive, by necessity, as few other
behavioral theories have generated more research.
The Reasoned Action
Perspective on Persuasion
Beliefs that people hold about a behavior play a
central role in reasoned action explanations of
behavior. In Fishbein and Ajzen’s (2010) words,
“human social behavior follows reasonably and
often spontaneously from the information or
beliefs people possess about the behavior under
consideration. These beliefs originate in a variety
of sources, such as personal experience, formal
education, radio, newspapers, TV, the Internet
and other media, and interactions with family
and friends. . . . No matter how beliefs associated
with a given behavior are acquired, they serve to
guide the decision to perform or not perform the
behavior in question” (p. 20).
When people act on beliefs that they have
formed about a behavior, they engage in a reasoned, but not necessarily rational process. For
example, someone suffering from paranoid personality disorder may lock the door of his office
because he believes that his colleagues are conspiring against him. This person acts in a reasoned
manner on a belief, even though others would
deem his belief irrational. Regardless whether
beliefs are irrational, incorrect (because based on
false information), or motivationally biased, once
beliefs are formed they are the cognitive basis from
which behavior reasonably follows (Blank &
Hennessy, 2012; Fishbein & Ajzen, 2010).
(c) 2013 Sage Publications, Inc. All Rights Reserved.
122——PART II. Theories, Perspectives, and Traditions
Beliefs affect behavior through a sequence of
effects. Specific beliefs about a behavior inform
attitude, perceived norm, and perceived behavioral control regarding the behavior, which in
turn determine intention to perform the behavior. If one has the necessary abilities to perform
the behavior and if there are no situational
obstacles that impede behavioral performance,
then intention should lead to behavior. The conceptualization of behavior formation as a process
makes clear that a persuasive message cannot
directly change behavior. Although the ultimate
objective of persuasive messages is to reinforce or
change a particular behavior, persuasive messages at best create or change beliefs. When
beliefs are appropriately selected, changes in
those beliefs should affect attitude, perceived
norm, or perceived behavioral control, which in
turn should affect intention and behavior. Those
beliefs that most strongly discriminate between
people who do and do not (intend to) perform a
particular behavior, are the choice candidates to
address in persuasive messages (Fishbein &
Ajzen, 2010; Fishbein & Yzer, 2003).
In terms of reasoned action theory, persuasion thus concerns the effects of exposure to a
persuasive message on beliefs about performing a
behavior, and through effects on those beliefs on
behavior. Clearly, then, the precision with which
one can predict behavior is directly relevant for
persuasion scholarship. The remainder of this
chapter will therefore be used to review the ability of reasoned action theory to predict behavior.
For this purpose it is useful to first discuss the
historical context in which reasoned action theory was developed.
Historical Context
In the early 20th century there was widespread
consensus that attitude should matter as a basis
for human behavior. For example, most contemporary definitions emphasized attitude as a tendency to act (for an overview see Allport, 1935).
By the 1960s, however, accumulated empirical
support for the hypothesis that people act on
their attitude was inconsistent at best, with many
studies reporting no effect of attitude on behavior at all. As a result, many scholars questioned
the usefulness of attitude for behavioral prediction. Most widely cited in this regard is Wicker
(1969), who, on a review of studies that correlated self-reported attitude with lagged observations of behavior, concluded that it is unlikely
that people act on their attitude. In counterpoint,
others argued that measurement issues were at
least in part responsible for weak correlations
between attitude and behavioral data. Particularly pertinent is Triandis’s (1964) finding that
the prediction of behavior from attitude
improved when measures of attitude and behavior represented the same dimensions.
The debate on the question whether attitude
predicts behavior helps understand the origins of
reasoned action propositions. In effect, what was
under discussion was whether contemporary
attitude theory offered valid hypotheses about
how thoughts, feelings, and behavior regarding
an object are associated. Fishbein observed that
the confusion surrounding the attitude-behavior
relation had to do with the wide range of different variables that were included under the
umbrella label of “attitude.” Similar to Thurstone
(1928), Fishbein (1967) viewed attitude as “a
relatively simple unidimensional concept, referring to the amount of affect for or against a psychosocial object” (p. 478). Building on Dulany’s
(1968) theory of propositional control over verbal responses, he argued that attitude should be
separated from its antecedents and consequences.
Moreover, in order to improve prediction of
behavior, he urged scholars to focus on the relations between these variables, that is, beliefs,
attitude, behavioral intention, and behavior
(Fishbein, 1963, 1967).
A number of principles have been developed
to aid such inquiry (e.g., Ajzen & Fishbein, 1973).
A first holds that prediction of behavior (e.g.,
running) is more precise than prediction of
behavioral categories (e.g., exercise) or goals (e.g.,
losing weight). Exercise includes many different
(c) 2013 Sage Publications, Inc. All Rights Reserved.
Chapter 8. Reasoned Action Theory——123
behaviors, and each of these behaviors may be
associated with quite different beliefs. From the
author’s perspective, for example, running is fun
but swimming is not. Whether or not I will report
to like and engage in exercise therefore depends
on whether I think about running, swimming, or
both when asked about my exercise. Similarly, losing weight is a goal that can be achieved by many
different behaviors, and one may hold positive
beliefs about losing weight yet in fact not achieve
that goal because necessary dieting and exercise
behaviors are not performed due to negative
beliefs about those behaviors.
Second, prediction of specific behaviors is
more precise than prediction of general behaviors. Levels of specificity vary by the extent to
which a behavioral definition includes each of
four components, that is, action (e.g., running),
target (e.g., at a 9-minute per mile pace), context
(e.g., on a treadmill at the YMCA), and time (e.g.,
twice a week). Clearly, “running” can be interpreted more broadly than “running twice a week
at a 9-minute pace on a treadmill at the YMCA.”
When two people think about “running,” they
may therefore think about quite different behaviors, each associated with different, behaviorspecific beliefs. It is for this reason that persuasive
messages are more effective when they promote a
specific behavior and its underlying beliefs than
a general, more broadly interpretable behavior
(Fishbein, 2000).
Third, and known as the compatibility principle, prediction of behavior improves when
behavior is measured at the same level of specificity as beliefs, attitude, and intention (cf. Triandis,
1964). For example, intention to recycle hazardous materials may not correlate with frequency
of recycling batteries, because people may intend
to perform the more general behavior of recycling hazardous materials but not intend to perform the specific behavior of recycling batteries.
Adherence to these principles should improve
the precision of behavioral prediction, and consequently, the effectiveness of persuasive efforts.
Remarkably, however, although these principles
are as relevant for the prediction of behavior
today as when they were first introduced, they
continue to be violated in research that applies
reasoned action theory (Hale, Householder, &
Greene, 2002; Trafimow, 2004). This has important implications. For example, it has been shown
that measurement in accordance with the compatibility principle strengthens relations among
reasoned action variables, which suggests that
studies that do not adhere to this principle
underestimate the ability of reasoned action variables to explain intention and behavior (Cooke &
Sheeran, 2004; van den Putte, 1993).
Key Components
and Their Relations
Reasoned action theory has three structural parts
that together explain behavior formation: (a) the
prediction of behavior from behavioral intention; (b) the explanation of intention as a function of attitude, perceived norm, perceived
behavioral control, and their underlying beliefs;
and (c) the exposition of beliefs as originating
from a multitude of potential sources. I will use
this partition to structure a discussion of issues
related to each reasoned action component and
the proposed relations between components.
Behavior
The precision with which behavior can be
predicted improves when specific behaviors
rather than behavioral categories or goals are
measured, and when the behavior that one
wants to predict is measured at the same level of
specificity as the variables that are used to predict it. Another noteworthy measurement issue
has to do with the question whether behavior
should be observed or assessed with self-report
measures.
Whereas for pragmatic reasons most reasoned
action research uses self-reports of behavior,
observed behavior has an intuitive appeal because
it does not, or at least to a lesser extent, suffer
(c) 2013 Sage Publications, Inc. All Rights Reserved.
124——PART II. Theories, Perspectives, and Traditions
from validity issues known to affect self-reports
of behavior (Albarracín et al., 2001). Key among
those is that self-reports of behavior can be exaggerated (e.g., male’s reports of sexual activity;
Brown & Sinclair, 1999) or understated (e.g.,
reports of at-risk health behavior; Newell, Girgis,
Sanson-Fisher, & Savolainen, 1999). Regardless
of whether these biases are deliberate or reflect
fallible cognitive estimation processes (Brown &
Sinclair, 1999), they render behavioral selfreports less than perfectly accurate. This does not
mean that prediction of observed behavior is
always more precise than prediction of selfreported behavior.
Consider, for example, Armitage’s (2005)
study of physical activity among members of a
gym. Armitage measured attitude, perceived
norm, perceived control, and intention at baseline with items framed in terms of “participating
in regular physical activity.” At a three-month
follow-up he assessed behavior by both asking
gym members enrolled in his study “How often
have you participated in regular physical activity
in the last 3 months?” and by electronically logging gym entrance. Clearly, baseline measures
were more compatible with the self-report
behavior measure than with the observed behavior measure. As just one example, when people
think about regular physical exercise, they may
think about activities outside the gym that are
not reflected in records of gym attendance, but
that likely are reflected in self-reports of physical
exercise. In support of this contention Armitage
found a stronger correlation of intention to participate in regular physical exercise with selfreported regular physical exercise, r = .51, than
with records of gym attendance, r = .42. This
finding has been corroborated in meta-analytic
research (Armitage & Conner, 2001; but see
Webb & Sheeran, 2006).
A moment’s reflection shows that the attitude,
perceived norm, perceived control, and intention
measures that Armitage used would have been
more compatible with, and thus more predictive
of, the self-report behavior measure used three
months after baseline if the former would have
asked about “participating in regular physical
activity in the next three months.” This is an issue
that affects many prospective studies. Interestingly, however, discussions about improving
behavioral prediction predominantly focus on
variables that possibly moderate effects of reasoned action variables on self-reported behavior,
and remain largely silent on measurement of
behavior itself (for a notable exception, see Falk,
Berkman, Whalen, & Lieberman, 2011). To be
sure, moderator analysis has important potential
for determining when the theory’s propositions
are particularly likely to apply, which not only
directs investigators to appropriate application
but also suggests areas for further theory development (Weinstein & Rothman, 2005). Even so,
the scarcity of work that tests the validity of
self-report behavior measures, for example, by
assessing compatibility between behavioral
determinant and behavior measures, is striking
(Albarracín et al., 2001).
Behavioral Intention
Behavioral intention is the most immediate
determinant of behavior. It is defined as people’s
readiness to perform a behavior: “Intentions are
assumed to capture the motivational factors that
influence a behavior; they are indications of how
hard people are willing to try, of how much of an
effort they are planning to exert, in order to perform the behavior” (Ajzen, 1985, p. 181). Intention is indicated by the subjective probability of
behavioral performance, that is, by people’s estimate of how likely it is that they will or will not
perform a particular behavior. Examples of
widely used intention items are How likely is it
that you . . . (followed by the definition of the
behavior under investigation; scale anchors
I definitely will not—I definitely will) and I intend
to . . . (scale anchors I completely disagree—
I completely agree).
The intention concept and its operationalization have not been universally accepted, however.
Concerned about the sufficiency of intention as
(c) 2013 Sage Publications, Inc. All Rights Reserved.
Chapter 8. Reasoned Action Theory——125
the only variable that directly determines behavior,
investigators have proposed several alternative
intention concepts and measures. This section
reviews three such measures.
Warshaw and Davis (1985) proposed that
behavioral expectations, or people’s selfpredictions regarding their behavior, are superior
to behavioral intention in predicting behavior,
because behavioral expectations take possible
barriers to behavioral performance into account
more so than intention. Items such as I expect
to . . . and I will . . . (scale anchors highly unlikely
to highly likely) are commonly used to measure
behavioral expectation. Empirical findings suggest that behavioral expectation measures do not
outperform intention measures (Armitage &
Conner, 2001; Fishbein & Stasson, 1990; Sheeran
& Orbell, 1998; but see Sheppard, Hartwick, &
Warshaw, 1988), and it is not uncommon to
combine the two types of measures into a single
intention scale (e.g., Fielding, McDonald, &
Louis, 2008).
Gibbons, Gerrard, Blanton, and Russell (1998)
proposed behavioral willingness as another alternative for intention. Gibbons and colleagues
argued that an intention to act implies rational
deliberation, whereas behavior often is irrational
and triggered by situational factors. Developed in
the context of health-risky behavior, the behavioral willingness hypothesis holds that people
may intend to engage in safe behavior, but be
willing to engage in risky behavior if the situation would offer opportunities for doing so. For
example, someone may intend to have no more
than three drinks at a party, but drink more when
at the party an attractive person offers a fourth
drink. Similar to this example, behavioral willingness measures ask whether people would be
willing to engage in a particular behavior given a
particular scenario, that is, under specified circumstances. It is therefore unclear whether
behavioral willingness is truly different from
intention or simply a more specific intention
(Fishbein & Ajzen, 2010).
Gollwitzer’s (1999) concept of implementation intentions offers a greater contribution to
behavioral prediction. Implementation intentions are highly specific plans people make about
when, where, and how to act on a motivation to
act, that is, on their intention to act. There is
evidence that implementation intentions improve
the prediction of behavior (e.g., Ziegelmann,
Luszczynska, Lippke, & Schwarzer, 2007), but not
always (e.g., Budden & Sagarin, 2007; for a
review, see Gollwitzer & Sheeran, 2006). Instead
of a viable alternative to the intention variable,
implementation intentions are perhaps better
interpreted as a useful moderator, such that
people who formed positive intentions are more
likely to act on their intentions if they have also
thought about how to implement their plans.
Predicting Behavior From Intention
Reasoned action theory has been able to
account for behavior with a good measure of
success. For example, meta-analyses of studies
that prospectively examined behavior found
intention-behavior correlations to average around
r = .45 (e.g., Albarracín et al., 2001; Armitage &
Conner, 2001; Cooke & Sheeran, 2004; Hagger,
Chatzisarantis, & Biddle, 2002; Sheeran & Orbell,
1998; Sheppard et al., 1998).Whereas these average correlations usefully indicate the theory’s
general ability to account for behavior, it is
important to understand which factors increase
or decrease the strength of association between
intention and behavior. Before discussing two
such factors, I first address an important methodological implication of the hypothesis that intention predicts behavior.
Testing Prediction
To test the hypothesis that intention predicts
behavior, behavior should be measured some time
after the variables that theoretically predict it were
measured. Because behavior assessed at a certain
time point indicates what people did at that same
time (for observed behavior) or have done prior to
that time (for self-reported behavior), correlating
(c) 2013 Sage Publications, Inc. All Rights Reserved.
126——PART II. Theories, Perspectives, and Traditions
cross-sectional intention and behavior data
produces a causal inference problem (Huebner,
Neilands, Rebchook, & Kegeles, 2011; Webb &
Sheeran, 2006; Weinstein, 2007). A cross-sectional
intention-behavior correlation indicates the extent
to which intention is consistent with people’s past
behavior, and should not be interpreted as prediction of future behavior. Unfortunately, inten­tionbehavior correlations obtained from cross-sectional
designs are still being published as tests of behavioral prediction (e.g., de Bruijn, Kremers, Schaalma,
Van Mechelen, & Brug, 2005; Keats, Culos-Reed,
Courneya, & McBride, 2007; Kiviniemi, VossHumke, & Seifert, 2007).
Lagged measurement is challenging, both for
methodological and budgetary reasons. It is therefore not surprising that cross-sectional studies
greatly outnumber prospective studies. For example, Albarracín and colleagues (2001) collected 96
samples for their meta-analysis, but of these, only 23
could be used to test the theory’s ability to predict
behavior. Similarly, Armitage and Conner (2001)
obtained correlations from 185 samples, yet only
44 of these provided lagged intention-behavior
correlations, and of the 33 samples that Cooke and
French (2008) analyzed, 19 could be used to
test intention effects on behavior (but see Hagger
et al., 2002, for a higher ratio). This means that
although reasoned action theory was designed to
predict behavior, it is primarily used to explain
intention. This gives pause for reflection: Despite
the thousands of reasoned action studies now in
existence, only a fraction provides a convincing
test of this key aspect of the theory.
Moderators of Intention
Effects on Behavior
At least two factors determine the strength of
intention-behavior relations. To begin, intention
should affect behavior to the extent that intention
is temporally stable. If between assessments of
intention and behavior nothing happens that
might change someone’s intention, then intention data should predict behavioral data. However, if intention changes between assessments
because, for example, someone is exposed to a
persuasive message, then the behavior data reflect
an intention formed after intention data were
obtained. The longer the gap between assessments of intention and behavior, the more likely
it is that intention changes, thereby attenuating
the intention-behavior correlation. Sheeran and
colleagues (Sheeran & Orbell, 1998; Sheeran,
Orbell, & Trafimow, 1999) found empirical support for this idea. For example, in a meta-analysis
of 28 prospective condom use studies, Sheeran
and Orbell (1998) found that intention-behavior
relations were stronger when the time between
measurement of intention and behavior was
short rather than long. Note, however, that there
is no gold standard for the optimal time lag
between intention and behavior assessments, in
part because it is near impossible to predict when
people will be exposed to factors that influence
their intention.
The relation between intention and behavior is
also conditional on actual control over behavioral
performance (Ajzen, 1985; Fishbein & Ajzen,
2010). People are thought to have actual control
over behavioral performance when they have the
necessary skills and when the situation does not
impose constraints on behavioral performance.
Thus, when despite positive intentions people do
not perform a behavior, behavioral nonper­
formance is not a motivational problem but a
problem of competence (i.e., deficient skills or
abilities) and means (i.e., presence of environmental constraints). It is here where the aforementioned implementation intentions prove useful;
actual behavior is more likely when people plan
how and when to act on their intention (Norman
& Conner, 2005; van Osch et al., 2009), possibly
because planning requires people to consider the
skills it takes and the obstacles they are up against
when they would perform a particular behavior.
Attitude and Behavioral Beliefs
Attitude is an evaluation of performing a
future behavior in terms of “favor or disfavor,
(c) 2013 Sage Publications, Inc. All Rights Reserved.
Chapter 8. Reasoned Action Theory——127
good or bad, like or dislike” (Fishbein & Ajzen,
2010, p. 78). Although attitude is typically analyzed with a single composite scale, attitude is
thought to have two aspects, namely an instrumental (or cognitive) aspect, indicated by perceptions of, for example, how foolish or wise,
useful or useless performing a behavior is, and an
experiential (or affective) aspect, indicated by
how unpleasant or pleasant, unenjoyable or
enjoyable performing the behavior is perceived
to be. The relative importance of instrumental
and experiential aspects of attitude as determinants of intention have clear implications for
persuasive messages; if instrumental attitude
matters most, a message should emphasize the
usefulness of the recommended behavior, but if
experiential attitude is more important, a message should emphasize how enjoyable the behavior is. Unfortunately, however, because published
reports often do not make clear whether attitude
was measured with instrumental, experiential, or
both types of items, inferences about when
instrumental and experiential attitude contribute
to behavioral prediction cannot be made with
full confidence. The question whether differential impact is predictable thus deserves more
systematic inquiry than it has received thus far.
According to reasoned action theory, attitude
formation is the process by which a potentially
large set of specific beliefs, which has associated
with a behavior over time, informs an overall
sense of favorableness toward the behavior. Consistent with expectancy-value perspectives, attitude is a multiplicative combination of behavioral
beliefs, which are perceptions of the likelihood
that performing a particular behavior will have
certain consequences, and an evaluation of those
consequences in terms of good or bad. For example, two persons may both believe that if they use
a tanning bed, they will get a tan. In addition,
person A thinks that being tanned is good, but
person B does not. In this single belief example,
both person A and person B think that using a
tanning bed will give them a tan, but because
their opposite evaluations of being tanned person
A’s attitude toward using a tanning bed is positive
and person B’s attitude is negative. This makes
clear that both beliefs about behavioral consequences and evaluations of those consequences
need to be considered to determine favorableness
toward a behavior. It also makes clear that to
change attitude, persuasive messages can address
beliefs about the likelihood of particular consequences of a behavior but also address evaluations of those consequences. For example, suppose
that people already believe that unprotected sex
may lead to gonorrhea but do not evaluate gonorrhea as a very serious disease. In this case, a message does not need to argue that unprotected sex
can lead to gonorrhea, but can improve attitude
toward using condoms if a message convinces
that gonorrhea is quite serious.
Although belief-evaluation product terms
have been found to correlate strongly with attitude (Albarracín et al., 2001), they typically do
not explain much more variance in attitude than
the separate behavioral beliefs (e.g., Armitage,
Conner, Loach, & Willetts, 1999). For this reason,
most investigators only assess behavioral beliefs,
or the perceived likelihood of behavioral consequences. Note, however, that for statistical reasons product terms are unlikely to be associated
with large effects in regression analysis, which is
the method commonly used to test reasoned
action (Ajzen & Fishbein, 2008; Yzer, 2007). We
should be careful not to abandon conceptual
ideas on the basis of empirical results if those
results reflect statistical artifacts.
Perceived Norm and
Normative Beliefs
To capture the influence of people’s social
environment on their intention to perform a
particular behavior, Fishbein and Ajzen (1975;
Ajzen & Fishbein, 1973; Fishbein, 1967) proposed the concept of subjective norm as a second determinant of behavioral intention. In the
theory of reasoned action (Fishbein & Ajzen,
1975) subjective norm is the extent to which
I believe that other people think that I should or
(c) 2013 Sage Publications, Inc. All Rights Reserved.
128——PART II. Theories, Perspectives, and Traditions
should not engage in a particular behavior.
Other scholars refer to subjective norm as
injunctive norm (Cialdini, Reno, & Kallgren,
1990), and in recent years, reasoned action theorists have used “injunctive norm” rather than
“subjective norm” to indicate expected approval
or disapproval from others (Fishbein, 2000;
Fishbein & …

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If you think we missed something, send your order for a free revision. You have 10 days to submit the order for review after you have received the final document. You can do this yourself after logging into your personal account or by contacting our support.

Prompt Delivery and 100% Money-Back-Guarantee

All papers are always delivered on time. In case we need more time to master your paper, we may contact you regarding the deadline extension. In case you cannot provide us with more time, a 100% refund is guaranteed.

Original & Confidential

We use several writing tools checks to ensure that all documents you receive are free from plagiarism. Our editors carefully review all quotations in the text. We also promise maximum confidentiality in all of our services.

24/7 Customer Support

Our support agents are available 24 hours a day 7 days a week and committed to providing you with the best customer experience. Get in touch whenever you need any assistance.

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Receive the final file

Once your paper is ready, we will email it to you.

Our Services

No need to work on your paper at night. Sleep tight, we will cover your back. We offer all kinds of writing services.

Essays

Essay Writing Service

No matter what kind of academic paper you need and how urgent you need it, you are welcome to choose your academic level and the type of your paper at an affordable price. We take care of all your paper needs and give a 24/7 customer care support system.

Admissions

Admission Essays & Business Writing Help

An admission essay is an essay or other written statement by a candidate, often a potential student enrolling in a college, university, or graduate school. You can be rest assurred that through our service we will write the best admission essay for you.

Reviews

Editing Support

Our academic writers and editors make the necessary changes to your paper so that it is polished. We also format your document by correctly quoting the sources and creating reference lists in the formats APA, Harvard, MLA, Chicago / Turabian.

Reviews

Revision Support

If you think your paper could be improved, you can request a review. In this case, your paper will be checked by the writer or assigned to an editor. You can use this option as many times as you see fit. This is free because we want you to be completely satisfied with the service offered.

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