Biomedical Engineering Reference
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Other research, however, provides evidence that speaks against these theories.
For instance, Cox et al. (
2010
) found that positive mood (e.g., media-induced affect)
actually elicits more nuanced processing of information. In their study, participants
in the neutral-mood condition were only infl uenced by side effect severity informa-
tion whereas those in the positive-mood condition used information about both
frequency and severity in forming their behavioral intentions. When side effects
were declared severe, participants did not prefer a product with more frequently
occurring side effects and perceptions of risk, rather than product effi cacy, drove
behavioral intentions. However, when side effects were declared mild—which is
often the case for many OTC and prescription medications—and participants were
in a positive mood, higher side effect frequency actually led to greater perceptions
(expectations) of product effi cacy. Undeniably, such effi cacy perceptions are likely
to have downstream effects (e.g., asking a doctor about initiating a regimen, seeking
additional information from other sources, and purchasing the product).
11.2.1.4
Message Presentation
Gain vs
.
loss frames
. Investigating the effect of gain and loss frames on people's
reaction to product risks, Cox et al. (
2006
) demonstrates that exposure to gain-framed
(vs. loss-framed) messages (e.g., people who get [don't get] the hepatitis B shot are
gaining [losing] a chance to protect themselves and the ones they love) increases
consumers' tolerance for temporary product risks (e.g., temporary skin rash from
using a lotion). Interestingly, however, regarding the possibility of more permanent,
serious risks (e.g., contracting Hepatitis B) people exposed to gain-framed (vs. loss-
framed) messages appear to exhibit considerable caution (e.g., more likely to get a
shot of vaccination).
Frequency vs
.
probability
. Research has shown an interesting connection between
the affect heuristic and message framing. In a study on clinicians' responses to risk
information, Slovic et al. (
2000
) demonstrated that when asked to evaluate the risk
of a mental patient committing a crime within 6 months of release from a hospital,
those presented with frequency information (e.g., “of every 100 patients similar to
Mr. Jones, 10 are estimated to commit an act of violence”) vs. those presented with
percentage information (e.g., “Patients similar to Mr. Jones are estimated to have a
10 % chance of committing an act of violence”) judged the patient to be more dan-
gerous. Through subsequent studies, these results were attributed to the idea that
frequency framed information elicited affect-laden imagery (e.g., envisioning many
violent patients) whereas probability-framed information resulted in less emotional
images (e.g., of one individual who is unlikely to commit a crime against others). In
another study, participants rated a disease that kills 1,286 people out of 10,000 as
being more dangerous than one that kills 24.14 % of people (Yamagishi
1997
).
Siegrist (
1997
) used an alternative measure—willingness to pay—to study the
effects of incidence rate format. Subjects were questioned about their willingness to
pay for an improved medication (a safer alternative); and risks associated with the
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