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Risk and Efficacy as Motivators of Change: Test of the Risk Perception Attitude (RPA) Framework
Unformatted Document Text:  The RPA Framework 19 Hypotheses H1A and H1C were supported, but H1B was not supported. Means are shown in Figure 3. Figure 3. Effects on Behavioral Intention Perceived Risk High Low BehavioralIntention .4 .2 -.0 -.2 -.4 -.6 Efficacy Beliefs Low High Discussion The primary objective of this paper was to determine whether prior findings from the RPA framework, which were based on people’s reactions to skin cancer information, could be replicated with information about diabetes. We specifically chose diabetes in order to test the RPA framework’s predictions in the context of a behavior about which participants had little prior knowledge. Indeed, prior knowledge about the behavior was quite low: the average score was 40.7 percent (SD = 18.5). For a multiple-choice test with four answer choices, the average correct response, chosen at random, would be 25 percent. After the manipulations, when participants were allowed to review information about diabetes, the average score jumped to 54.7 percent (SD = 17.2), and this was statistically significant, t = 7.0, p < .0001. Hence, our rationale for choosing to focus on diabetes (expectation that prior knowledge would be low) seems to have

Authors: Rimal, Rajiv., Morrison, Dan. and Mitchell, Monique.
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The RPA Framework
19
Hypotheses H1A and H1C were supported, but H1B was not supported. Means are shown in
Figure 3.
Figure 3. Effects on Behavioral Intention
Perceived Risk
High
Low
B
e
h
a
v
i
o
r
a
l
I
n
t
e
n
t
i
o
n
.4
.2
-.0
-.2
-.4
-.6
Efficacy Beliefs
Low
High
Discussion
The primary objective of this paper was to determine whether prior findings from the
RPA framework, which were based on people’s reactions to skin cancer information, could be
replicated with information about diabetes. We specifically chose diabetes in order to test the
RPA framework’s predictions in the context of a behavior about which participants had little
prior knowledge. Indeed, prior knowledge about the behavior was quite low: the average score
was 40.7 percent (SD = 18.5). For a multiple-choice test with four answer choices, the average
correct response, chosen at random, would be 25 percent. After the manipulations, when
participants were allowed to review information about diabetes, the average score jumped to 54.7
percent (SD = 17.2), and this was statistically significant, t = 7.0, p < .0001. Hence, our rationale
for choosing to focus on diabetes (expectation that prior knowledge would be low) seems to have


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