Vol.11 no.4 1996
Pages 453-461
HEALTH EDUCATION RESEARCH
Theory & Practice
Predicting the intention to eat healthier food among
young adults
Lisbet 0ygard and Jostein Rise
Abstract
The purpose of this study was to investigate
which factors predicted the intention to eat
healthier food. The empirical data stem from a
questionnaire survey carried out among 527
young adults aged 23-26 years living in Oslo,
Norway. The study was carried out in September
1991. The Theory of Planned Behavior (TPB)
was used as a guiding theoretical framework.
The components of the TPB accounted for 32%
of the variance in behavioral intention. Attitude
was the strongest predictor, followed by perceived behavioral control. Subjective norm
received the lowest weight A detailed analysis
of the underlying cognitive structures revealed
that the outcomes which discriminated most
strongly between those who intended to eat
healthier food, those who were undecided and
those who had no intention, were that healthier
food would improve the shape of the body,
increase enjoyment of food and reduce weight
In addition, the control beliefs 'weight', 'able
to make healthier dishes', 'social eating' and
'busy' discriminated mostly between the three
intender groups. These outcomes might preferably be addressed in persuasive communications
to change intentions to eat healthier food.
Introduction
Evidence abounds that unsatisfactory diets contribute to increased risk of contracting a number of
Research Center for Health Promotion, University of
Bergen, Oistensgate 3, N-5007 Bergen, Norway
O Oxford University Press
detrimental conditions (James etal., 1988). Despite
this compelling evidence, a substantial proportion
of the population continues to consume unsatisfactory diets in terms of, for example, food containing
too much fat, sugar and salt Furthermore, studies
have demonstrated that the fat content of the diet
has increased progressively in European countries
over the last 15-20 years (James et al., 1988;
WHO, 1990). Although this trend seems to have
stabilized since 1980, this was only the case where
the fat intake was already very high, e.g. in Norway.
Considering salt intake, European adults of both
genders are eating approximately twice the 5 g
salt per day that WHO considered as a goal for
national averages (WHO, 1990). Sugar consumption is clearly excessive in many countries, with
most European countries lying above the ideal goal
of sugar intake, including Norway. Compared to
all European countries, people in northern countries
and the UK eat less vegetable products and fruits
(WHO, 1990).
The main strategy which seems to be relied
upon by public authorities responsible for nutrition
education is provision of traditional information
and advice about the relationship between diet
and health. Although these efforts have obviously
contributed to increased nutrition knowledge and
awareness, there is growing evidence that people
do not translate this kind of knowledge and
awareness into motivations to act in a more healthy
direction (for review, see Sheeshka et al., 1993;
Wardle, 1993). As noted by Wardle (1993) the
available evidence seems to have led to a devaluation of nutrition education as a public health tool
and to the discouragement of the role of cognitive
factors in research on dietary choices. On the
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L.0ygard and J.Rise
other hand, one might argue that the educational
strategies have failed to take into account the entire
range of relevant cognitions upon which people
base their dietary choices. Health beliefs, as defined
in a narrow sense, may have only negligible
contributions to the service of behavioral change.
Nevertheless, knowledge related to nutrition issues
remains important in health education strategies
based upon informed choices.
Theories of Reasoned Action and Planned
Behavior
The Theory of Planned Behavior (TPB) is an
extension of Fishbein and Ajzen's Theory of
Reasoned Behavior (TRA) (Fishbein and Ajzen,
1975; Ajzen and Fishbein, 1980; Ajzen, 1988).
TRA posits that the performance of a particular
behavior is seen as determined by intention to
perform it Behavioral intention is in turn a function
of two factors. First, the person's attitude towards
the behavior, which refers to the extent to which the
person has a favorable or unfavorable evaluation
of the behavior. Second, the subjective norm or
perceived social pressure to perform or not perform
the behavior. These two factors are underpinned
by sets of beliefs. For the attitude component the
beliefs are behavioral beliefs concerned with the
perceived likelihood that performing the behavior
will lead to certain outcomes multiplied by the
extent to which these outcomes are valued. For
the subjective norm component, the beliefs are
normative beliefs focusing on die perceived social
pressure from certain referents multiplied by the
person's motivation to comply with these referents.
The assumption of TRA, that all relevant social
behaviors are under volitional control, has been
challenged in terms of not being applicable to
behaviors which are subject to outside control in
terms of resources, cooperation and skills (Ajzen,
1991). Thus Ajzen proposed to include the concept
of perceived behavioral control into TRA, which
is compatible with Bandura's concept of selfefficacy in terms of 'judgment of how well one
can execute courses of action required to deal with
prospective situations' (Bandura, 1982). Bandura
(1986) has suggested that self-efficacy varies along
454
the dimensions of magnitude, generality and
strength. Kok et al. (1991) suggested that if one
measures a person's perceived ability to perform
a particular behavior in a number of different
situations then one captures the dimensions of
both strength and magnitude. This measurement
procedure was adhered to in this study, but nevertheless we also adhered to the notational convention
of TPB denoting the concept of perceived
behavioral control.
TPB therefore posits that to the extent that
people intend to engage in more healthy eating,
they need to believe that they have control over
performance of these specific behaviors, in addition
to what they personally get out of it (attitude)
and others' approval of it (subjective norm). The
relative importance of the three components is
assumed to differ with regard to the particular
behavior in question and the target population.
Fishbein and Middelstadt (1987, 1989) have
argued for the utility of the TRA in the development
of successful health education programs. They
suggest that the more one knows about the factors
underlying a decision to perform a particular
behavior, the greater the probability that one can
change that behavior. From this perspective, TPB
is even more useful since it also concentrates on
control beliefs. The first step is to identify whether
the particular behavior is influenced by attitude,
subjective norm or behavioral control. In the second
step, changing the behavioral intention becomes a
matter of producing change in accessible beliefs
or introducing new beliefs. Thus it has been
recommended to perform a molecular analysis of
the beliefs and evaluations underlying the components of the model as a strategy for constructing
persuasive messages which should be targeted in
a communication directed to potential message
recipients (Fishbein and Middelstadt, 1987, 1989;
Sutton et al., 1990; Eagly and Chaiken, 1993).
The present paper deals with these two issues
related to healthy eating behavior.
Previous research
The original TRA has been used in a number of
earlier studies to focus on behavioral intentions
Predicting intentions to eat healthier food
related to performance of a number of dietary
behaviors in terms of selection of milks with
varying fat contents (Tuorila, 1987), selection of
low salt bread (Tuorila-Ollikainen et al., 1986),
eating at a fast-food hamburger restaurant (Axelson
et al., 1983) and consumption of high-fat foods
(Shepherd and Stockley, 1985; Tuorila and
Pangbom, 1988). The latter study also included a
number of other nutritional behaviors like milk,
cheese, ice cream and chocolate. The most consistent finding from these studies was that attitudes
was a better predictor of behavioral intention and
actual behavior than subjective norms. Furthermore, a number of studies which have included
self-efficacy (or perceived behavioral control) have
demonstrated that this factor was an important
predictor of eating behavior (Slater, 1989; Shannon,
1990). On the other hand, a study concerning
intention to eat wholemeal bread indicated that the
addition of perceived control did not contribute to
a significant improvement of behavioral intention
(Sparks, 1991).
A number of studies have also investigated
which specific beliefs distinguish between
intenders and non-intenders to provide better
information about the cognitive structure underlying the nutritional decision (Brinberg and
Durand, 1983; Axelson et al., 1983; Shepherd,
1988). In relation to election of high-fat foods the
most consistent finding was the discrimatory power
of hedonic preference in terms of enjoyment of
taste (Shepherd and Stockley, 1985; Tuorila and
Pangbom, 1988). However, a more systematic
analysis of the underlying cognitive motivations,
including expectancy value reasoning to provide
clues to which cognition might be targeted in a
persuasive communication (see Sutton etal., 1990),
seems to be lacking.
Research questions
In the present study, two research goals were
posed. First, to report the relative contribution of
attitude, subjective norm and perceived behavioral
control in predicting the decision to eat healthier
food. Second, to give a detailed analysis of the
underlying cognitions with particular reference to
which of them discriminated between those who
intend, those who had not decided and those who
did not intend to eat healthier food.
Methods
Sample
This study is part of the 'Oslo Youdi Study', a
longitudinal study which started in 1979 designed
to obtain epidemiological data about cardiovascular
disease, cancer risk factors and related behaviors
among adolescents (Tell, 1987). Six schools in
Oslo participated {N = 1016), and three of the
schools received an intervention program focusing
on eating patterns, smoking behavior and physical
activity. The main objectives of the nutrition program were to reduce students' intake of sugar, salt
and fat, and to increase their intake of complex
carbohydrates. In September 1991, the same subjects, now 23-26 years of age, were invited to
participate in a follow-up study. A total of 703
subjects participated (339 males and 264 females)
(participation rate of 75.1%). Results presented
elsewhere demonstrated that there was no longterm impact of the nutrition education program
(Klepp et al., 1994), and in this paper intervention
and control subjects have been combined. Data
presented in this paper stem from the 1991 investigation.
Variables
In order to yield accurate prediction, constructs of
the model should correspond with regard to action,
target, context and time (Ajzen and Fishbein,
1980). We have identified the behaviors in terms
of eating (action) healthier food (target) during the
next 4 weeks (time). The context factor was not
specified.
The respondents were required to define 'healthy
food' as 'foods containing a low quantity of fat,
sugar and salt'. The dependent variable, behavioral
intention, was measured by means of the question:
'How likely is it that you will eat healthier food
during the next 4 weeks?', using a five-point
probability scale ranging from very likely (1) to
very unlikely (5). One problem with the dependent
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L.0ygard and J.Rise
variable might be that people who are eating
healthy food have no plans to change and thus
would be classified as having low intention. To
solve this problem, we constructed an index consisting of 17 healthy and unhealthy food items,
and the subjects with the healthiest eating patterns
(25%) were excluded from the analyses. The
sample was therefore reduced from 707 to 527
individuals. For the analysis of the underlying
cognitions, participants were divided into three
categories: (1) intenders (N = 99), (2) neutrals
(those who reported they had not decided) (N =
221) and (3) non-intenders (N = 204).
Behavioral beliefs
In order to obtain the salient beliefs for the study,
seven beliefs were elicited and assessed in terms
of five-point probability scales from very unlikely
(1) to very likely (5): 'For me to eat healthier
food during the next 4 weeks will: decrease the
cholesterol level ('cholesterol'), reduce my risk of
cancer ('cancer'), reduce my risk of heart disease
('heart'), get me into better shape ('shape'), help
me reduce weight ('weight'), make me look young
longer ('younger') and make me enjoy the food
more ('enjoy')'.
also had 'do not know' and 'do not have' categories. Parents, friends, partners, siblings and coworkers represent groups salient to individuals, in
that they arc expected to have a pre-eminent
influence on consumer behavior (Zaltman and
Wallendorf, 1979; Loudon and Bitta, 1993).
Doctors do not usually represent a salient group,
but it is interesting to investigate whether they
have any influence on this particular action.
Motivation to comply
Motivation to comply with the above referents was
measured by means of the following question:
'How important is it for you to comply with ...?'
in terms of a four-point scale from not important
at all (1) to very important (4). The strength
of each normative belief was multiplied by the
corresponding motivation to comply. The resulting
products were summed across all salient referents
to constitute subjective norm (Cronbach's a =
0.79).
Perceived behavioral control
Evaluation of the outcomes listed above was measured in terms of 'How important is ...' ranging
from not important at all (1) to very important (4).
Subsequently, the probability that each outcome
would occur under healthier eating was multiplied
by the corresponding behavioral belief. The
resulting products were summed across all outcomes to constitute an indirect measure of attitude
(Cronbach's a = 0.76).
Control expectations were assessed by nine question measuring people's beliefs in eating healthier
food in several specific prospective situations.
Participants were asked to evaluate to what extent
they felt able to eat healthier food when: depressed,
alone, busy, tired, trying to reduce weight
('weight'), unhealthy food tastes better ('taste'),
with people who eat unhealthy food ('social eating') and at parties where both healthy and
unhealthy food are available ('in parties'). In
addition, a global question was asked about the
extent to which they felt able to prepare healthier
dishes ('able'). The internal consistency of the
efficacy scale was 0.85.
Normative beliefs
Statistics and procedures
A five-point probability scale was used to measure
the extent to which the participants believed that
parents, friends, partners, siblings, doctor and coworkers/student fellows ('co-workers') thought
they should engage in healthier eating: 'Do you
believe that your ... think you should eat healthier
food during the next 4 weeks?1. These variables
Pearson's r was used to investigate the relationship
between attitude, subjective norm, perceived
behavioral control and behavioral intention.
Because attitude, subjective norm and perceived
behavioral control were intercorrelated, we used
hierarchical, multivariate regression analysis to
examine the unique associations of each component
Outcome evaluations
456
Predicting intentions to eat healthier food
of the model with behavioral intention. Firstly, the
components of TRA were entered, attitude and
norm; secondly, perceived behavioral control was
included. One-way analysis of variance was used
to study which beliefs discriminated intenders,
neutrals and non-intenders to eat healthier food
during the next 4 weeks. These associations were
quantified using T| values P values.
Gender has been linked to a variety of health
behaviors, including healthy eating (see Waldron,
1988). In the present study, there was little evidence
that the differences between the intender groups
varied depending on their gender. Only two significant interactions were obtained in two instances:
gender by intention interaction on the outcome
evaluation of how important the participants rated
enjoyment of food and motivation to comply with
the doctor. Hence there was no empirical rationale
for presenting gender-specific results.
Table I. Correlations (Pearson i x) among attitude, subjective
norm, perceived behavioral control ('control') and intention to
eat healthier food during the next 4 weeks (N => 527)
Attitude
Subjective norm
Control
Subjective nonn Self-efficacy
Intention
0.30"
0.51**
0.23**
037**
0.36**
0.06
**P < 0.001.
Table IL Prediction of intention to eat healthier food during
the next 4 weeks from attitude (A), subjective norm (SN) and
perceived behavioral control (PBC): hierarchical multivariate
regression analysis (N •= 527)
A+SN
A+SN+PBC
R
Adj. R2
Bt
Ba
fipfcc
0.51
0.56
0.26
0.32
0.49**
0.38**
0.07
0.10* 0.26**
•P < 0.01; • • / » < 0.001.
Results
Correlations between behavioral intention,
attitude, subjective norm and perceived
behavioral control
Table I shows that behavioral intention was significantly associated with attitude (r = 0.51), subjective norm (r = 0.23) and perceived behavioral
control (r = 0.37). In addition, there were substantial correlations between some of the concepts in
the model. Attitude correlated with both subjective
norm (r = 0.30) and perceived behavioral control
(r = 0.36).
The relative contribution of attitude,
subjective norm and perceived behavioral
control
Table II demonstrates the relative contribution of
attitude, subjective norm and perceived behavioral
control in predicting behavioral intention in terms
of standardized regression coefficients. Hierarchical regression analyses showed that the constructs
of TRA accounted for 26% of the variance in the
decision to eat healthier food during the next 4
weeks. When perceived behavioral control was
included in the equation, the explained variance
increased to 32%. Given that the P weights in the
last step can be used as estimates of the relative
contribution of the three components, it can be
seen that attitude was the most important, followed
by perceived behavioral control. Subjective norm
was insignificant in the first step, although it
retained its statistically significant effect throughout (P = 0.10).
Analyses of the underlying cognitions
Univariate tests (one-way analysis of variance) of
the individual items demonstrated that for all items
intenders rated the outcomes as significantly more
important than both neutrals and non-intenders. The
greatest difference between the three categories
occurred for 'shape' (T| = 0.38), 'enjoy' (T| =
0.38) and 'weight' (T| = 0.24). Regarding the
products of belief strength and their outcome
evaluation of healthy eating, 'shape' discriminated
strongest between the intender groups (T) = 0.43)
followed by 'enjoy' (r| = 0.40) and 'weight'
(11 = 0.39).
Table IV demonstrates the cognitions underlying
subjective norm. There was a consistent tendency
in the direction that intenders believed that it was
457
L.0ygard and J.Rise
Table Hi- Mean scores of beliefs strength (BS) and outcome evaluation (OE) BS x OE/or intenders (I), neutrals (N) and nonintenders (NI), and r\ coefficients
BS
Heart
Cholesterol
Cancer
Shape
Weight
Young
Enjoy
BS X OE
OE
I
N
NI
n
I
N
NI
n
I
N
NI
n
4.0
3.8
3.6
4.1
3.7
33
32
3.8
3.5
32
3.6
32
3.0
Z7
3.5
33
2.8
3.0
2.6
2.5
2.1
0.20**
0.19*
0.26**
038**
034**
0.27**
038**
3.4
2.7
3.6
33
2.6
32
32
3.1
2.5
3.4
3.0
2.1
2.9
3.1
2.9
23
3.2
2.8
1.8
2.7
2.8
0.22**
0.21**
0.20**
027**
0.33**
0.21**
0.22**
13.5
10.5
12.8
14.1
10.9
10.7
10.3
12.2
9.1
11.1
11.0
10.3
7.7
9.1
8.6
5.2
7.0
5.9
0.25"
025**
0.19**
0.43**
0.39**
0.30**
0.40**
7.0
8.8
8.8
•P < 0.01; **P < 0.001.
Table IV. Mean scores for normative beliefs (NB) and motivation to comp/y fAfC) and NB :< MCfor intenders (1), neutrals (N)
and non-intenders (SI), and V| coefficients
NB
Parents
Partner
Friends
Doctor
Siblings
Co-workers
BS X OE
MC
I
N
NI
T|
I
N
NI
n
I
N
NI
1
2.9
2.8
2.5
2.8
2.4
2.2
2.4
2.5
2.1
2.6
2.1
2.0
22
2.6
1.9
2.4
2.0
1.8
0.23***
0.20***
0.21***
0.15*
0.12*
0.18**
23
3.6
2.5
3.6
2.6
2.1
23
3.4
23
35
2.4
2.2
2.2
3.5
27
3.5
2.4
2.3
0.07
0.07
0.13*
0.06
0.08
0.07
6.8
10.0
6.2
9.9
6.2
4.5
5.6
8.5
4.7
9.1
4.9
45
4.7
7.9
4.2
8.2
4.7
4.2
0J21***
0.19**
0.25"*
0.16*
0.16*
0.06
*P < 0.05; **P < 0.001; ***P < 0.0001.
more likely that the referents expected them to
improve their healthy eating than the other two
categories. There were significant differences
regarding the normative beliefs of all referent
groups. However, the differences between the three
intender groups concerning motivation to comply
with referents were small or insignificant. Regarding the products consisting of normative beliefs
and motivations to comply, the results indicate
that 'friends' (t| = 0.25) discriminated strongest
between the intender groups, followed by 'parents'
and 'partners' (for both groups, TJ = 0.21).
Table V demonstrates the control beliefs for the
intender groups. The beliefs which discriminated
most between the intender groups were, in descending order 'weight' (j\ = 0.29), 'able' (T| =
0.29), 'social eating' (T| = 0.23) and 'busy' (T| =
0.22). However, for all items intenders expressed
458
significantly higher confidence in their ability to
change their eating behaviors in a more healthy
direction than neutrals and non-intenders.
Discussion
This study has shown the advantage of TPB as
compared with TRA in accounting for the
intention to improve one's healthy eating in
terms of a significant increase in multiple R
from 0.51 to 0.56. However, this level of
prediction is below the usual figures observed
in studies using TPB (Ajzen, 1991). Generally,
the low predictive ability of the model could
not be explained by a violation of the rules of
correspondence, thus each component of TRA
was assessed in terms of action (eat), target
(more healthy food) and over a range of occasions
Predicting intentions to eat healthier food
Table V. Mean scores for control beliefs for Menders (I),
neutrals (N) and non-intenders, and r\ coefficients
Weight
Able
Alone
Taste
Tired
Social eating
Busy
At party
Depressed
I
N
Nl
n
3.1
3.6
2.9
Z6
2.4
2.7
2.4
2.2
23
2.7
3.0
2.5
23
12
2.6
2.2
23
23.
15
2.8
2.3
2.1
1.9
2.2
1.9
25
2.0
029"
0.29**
0.19"
0.17**
0.23"
0.23"
0.22"
0.14*
0.13*
*P < 0.01; **P < 0.001; P < 0.0001.
(time; next 4 weeks), while context was left
unspecified. However, perceived behavioral control was measured by specifying one's ability to
perform the behavior in a number of situations,
while no reference was made to a specific time
interval. This procedure makes the concept of
perceived behavioral control a more global
construct than attitudes and subjective norms in
terms of being specific only with regard to the
particular action involved. This circumstance
might have underestimated its predictive ability
in relation to intention.
The relative contribution of attitude,
subjective norm and perceived
behavioral control
The results of this study corroborate those of
other studies related to predicting intentions of
healthy eating in that attitudes turned out to be
a stronger determinant of behavioral intention
than subjective norm (Shepherd and Stockley,
1985; Tuorila, 1987; Shepherd, 1988; Rosin
et al., 1992). Thus the decision of whether to
engage in healthier eating in this group of young
adults is determined more by calculation of
personal pay-offs than by what they believe
others may think of this issue. This conclusion
is at variance with the results presented by
Kristal et al. (1990), who observed that the
selection of low-fat diets was predictable from
perceived norms and, to a lesser extent, from
attitudes. However, one should bear in mind that
their conceptualization of norms in terms of
perceptions of the opinions of others (termed
interpersonal norms) and appropriateness about
specific behaviors (termed behavioral norms)
deviated from the normative component of TRA
(or TPB). This might indicate that the normative
component of TRA in a number of behavioral
situations suffers from limited theoretical elaboration and faulty measurement
As noted above, several studies predicting
healthy eating behaviors have adopted the social
cognitive theory as their guiding theoretical
framework (Shannon, 1990; Sheeshka et al.,
1993). A consistent observation of these studies
is that self-efficacy (or perceived behavioral
control) is a stronger determinant than outcome
expectations and in one study (Sheeshka et al.,
1993) this concept also appeared to mediate the
effect of a number of social variables. The
results of this study confirm the importance of
perceived behavioral control for the prediction
of healthy eating intentions in terms of having
significant regression weights (p* = 0.26). On
the other hand, attitudes—denoted outcome
expectations in the language of social cognitive
theory—appeared to be a stronger determinant
of intention than perceived behavioral control.
However, as discussed above, the effect of
perceived behavioral control may have been
underestimated as compared with attitudes and
subjective norm in terms of being operationalized
as a more global cognition than the other two.
Implications for health education
The detailed study of the underlying cognitive
structures provides us with information about
which cognition should be targeted in a persuasive
communication. First, those beliefs which discriminated between those who intended to eat healthier
food, the neutrals and those who did not intend
to do so are candidates for such communications.
In addition, such an analysis gives clues about
which beliefs should not be addressed, i.e.
information which is already part of their
cognitive structure. According to expectancy
value theory, subjective probabilities determine
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L.0ygard and J.Rise
which direction the behavior will take, while
evaluations indicate whether this particular
behavior will occur. Believing that healthy eating
reduces the risk of heart disease does not in
itself guarantee that a person will pursue this
particular course of action; in addition, the risk
reduction of, for example, heart disease should
be highly valued. In mathematical terms this
assumption proposes that subjective probabilities
and evaluative ratings are multiplied. This
reasoning means that the product scores of
the various single items (outcomes) constitute
incentives and disincentives for engaging in
healthier eating (Sutton et al., 1990).
In the construction of persuasive messages to
strengthen or increase the intention to engage in
healthier eating, the most promising candidates
would preferably be those outcomes which
discriminated strongest between intenders and
non-intenders, i.e. 'shape', 'enjoy' and 'weight'.
This group of beliefs may be linked to a concern
with one's appearance and seems not to be
emphasized in traditional nutritional campaigns,
where nutritional beliefs dominate as arguments.
On the other hand, this study does not indicate
that messages related to improvements in health
are redundant information. Thus the cognition
related to outcomes which have health implications do indeed have motivational properties for
healthier eating behavior. Health beliefs like 'heart
disease' and 'cancer' significantly differentiated
between intenders, neutrals and non-intenders,
but not to the same extent as outcomes related
to concern about appearance. There is thus no
reason to exclude traditional health risk information from messages in nutritional campaigns
aimed at improving healthier eating behaviors.
The third category of outcomes which could
possibly be included in a nutritional persuasive
message are those related to hedonic preference
in terms of enjoyment of taste. The importance
of hedonic preference or liking factor in food
choices is a consistent finding (Tuorila-OUikainen
et al., 1986; Wardle, 1993). Thus the message
should be constructed so as to attempt to
convince people that healthy food may also be
460
tasty. In fact it has been suggested to include
hedonic response or overall liking in TRA to
improve its predictive power in relation to foodrelated behaviors (Tuorila-OUikainen, 1986).
Our findings also indicate that control beliefs
preferably might be included in nutritional
persuasive messages in order to enhance people's
confidence in their ability to eat healthier food.
Given that the most important control beliefs are
'weight', 'able', 'tired', 'social eating' and 'busy',
nutritional health campaigns should probably also
address such situations.
The fact that the indirect measure of subjective
norm received the lowest regression weight
for predicting behavioral intention makes this
component of less interest for providing insight
into the motivation to engage in healthy eating
and hence for arguments which can be used ih
a persuasive communication to alter this intention.
Conclusion
This study has shown that the ability of the
components of the original TRA to predict
behavioral intention in terms of eating healthier
food was relatively low. The inclusion of
perceived
behavioral
control
significantly
improved the predictive power, thus demonstrating
the advantage of the TPB as compared to TRA.
Consistent with previous research on nutritional
behaviors, attitude was a stronger determinant
than subjective norm. A more finely grained
analysis of the individual cognitions also suggests
that there is no reason to exclude traditional health
motives from persuasive nutritional messages. In
addition, the importance of hedonic preferences
and concern about appearance and control beliefs
in relation to healthy food choices should be
paid attention to when constructing persuasive
nutritional messages.
Acknowledgements
Funding for this study was provided by the
Norwegian Cancer Society and the Norwegian
Research Council.
Predicting intentions to eat healthier food
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Received June 20. 1995; accepted November 19. 1995
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