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Perhaps the most important moderator of the BI / behavior relation is the nature of the behavior involved. In particular, four
dimensions of behavior influence the predictive power of the BI construct: a) perceived behavioral control, b) complexity, c) social
desirability, and d) social involvement.
Perceived behavioral control. Recognizing
that perceived ability to perform a particular behavior, or
achieve a certain goal may influence whether the behavior
actually occurs, Ajzen
(1991)
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Ajzen, I. (1991). The theory of planned behavior. Organizational
Behavior and Human Decision processes, 50, 179-211.
developed the TPB, which adds a self-efficacy component to
the TRA, called perceived behavioral control (see
Measures). When perceived and actual control are high,
BI should relate directly to outcome. When the behavior is
difficult, however—sticking to a diet, for example,
or avoiding fatty foods—intentions may be high, but
ability may be a step or two lower. Meta-analyses have suggested
that this additional construct adds about 2% on average to
the percentage of variance accounted for in behavior (Armitage
& Conner, 2001
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Armitage, C. J., & Conner, M. (2001). Efficacy of the
theory of planned behaviour: A meta-analytic review. British
Journal of Social Psychology, 40, 471-499. ).
That amount does vary considerably, however, depending on
the actual difficulty involved—up to a high of 12% for
behaviors such as quitting smoking, which are very difficult
(Godin,
Valois, Lepage, & Desharnais, 1992
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Godin, G., Valois, P., Lepage, L., & Desharnais, R. (1992).
Predictors of smoking behaviour: An application of Ajzen's
Theory of Planned Behaviour. British Journal of Addiction,
87, 1335-1343.).
Complexity. Multiple-act criteria,
i.e., behaviors that require a series of actions to complete
(e.g. fecal occult blood test or FOBT), are more difficult
to predict than are less complex behaviors. One reason for
this is that people tend to overestimate the likelihood that
they will successfully complete all of the actions in the
series, when failure to complete any one of them stops the
behavior. Thus, intentions don't do as good a job (relatively
speaking) in predicting complex behaviors like screening for
cancer. For example, Godin
and Kok (1996)
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Godin, G., & Kok, G. (1996). The theory of planned behavior:
A review of its applications to health-related behaviors.
American Journal of Health Promotion, 11, 87-98.
and McEachan
and Conner (2005)
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McEachan, R. & Conner, M. (2005). A meta-analysis of cancer-screening
behavior. Unpublished manuscript. both found that
BI explained about 16% of the variance in screening behavior,
meaningfully lower than researchers typically observe for
other (less complex) behaviors.
Social desirability. Ajzen
and Fishbein (2005)
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Ajzen , I., & Fishbein, M. (2005). The influence of attitudes
on behavior. In. D. Albarracin, B. Johnson, & M. Zanna
(Eds.) Handbook of attitudes and behavior.
refer to the issue of poor BI "performance" as "literal
inconsistency" - the tendency for people to not
do what they said they were going to do. This is especially
likely when the behavior is very high or very low in social
desirability. Most instances of poor BI prediction involve
the former: reports of intentions to do appropriate behaviors
that don't actually result in action. For example, Sheeran
(2002)
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Sheeran, P. (2002). Intention-behavior relations: A conceptual
and empirical review. In W. Stroebe & M. Hewstone (Eds.),
European review of social psychology, Vol. 12 (pp.
1-36). Chichester, England: Wiley. reported that people
who say they do not intend to engage in cancer screening very
seldom do (what he calls "behavioral inertia"); however, a
significant percentage of those who give the socially desirable
response—"I intend to screen"—do not follow
through. More generally, Sheeran found across a variety of
health behaviors, that the median percentage of people who
said they did not intend to "do the right thing" and
did not was 93%, whereas about half of those who said they
had good intentions never acted on those intentions. Presumably,
the same problem exists, in reverse, for undesirable behaviors—low
reported BI, but performance nonetheless. A recent meta-analysis
(Webb & Sheeran, 2006) addressed this issue, by looking
at health risk as well as health promotion behaviors.
Social involvement. Webb and Sheeran
(2006) conducted a meta-analysis of the BI / health behavior
relation, examining only those studies (N = 47) that included
longitudinal measures of BI and behavior, and involved
an intervention intended to change the former (BI), in
an effort to then change the latter. They concluded that changes
in health intentions had a smaller impact on changes in health
behavior (i.e., Δ BI / Δ behavior relations were
weaker) when: a) the gap between measurement of BI and behavior
was relatively long (greater than the median of 11.5 weeks),
b) the behavior included a significant habitual component
(e.g., seat belt use), c) perceived and actual control were
low, and d) the behaviors involved health risk (as opposed
to health promotion) and were performed in "social contexts"
(e.g., smoking, condom use). The authors concluded that intentional
control over health behavior is more limited than previously
thought. They also recommend that future behavior change efforts
give greater consideration to non-intentional routes to health
behavior that include health images or prototypes (see Gibbons
& Gerrard, 1997
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Gibbons, F. X., & Gerrard, M. (1997). Health images and
their effects on health behavior. In B. P. Buunk, & F.
X. Gibbons (Eds.), Health, coping, and well-being: Perspectives
from social comparison theory. (pp. 63-94). Mahwah, NJ,
US: Lawrence Erlbaum Associates, Publishers. ) and
"automatic" (i.e., situationally-controlled) processes.
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