Risk Assessment: The Consumer as an Intuitive Statistician - Innovation and Marketing in the Pharmaceutical Industry - page 290

However,

PerceivedRisk=+ + a Ye S

i i

where

ψ = identification of the presence/absence of a symptom, risk behavior, or preventive
behavior
β = the perceived diagnosticity of each symptom/behavior
α = feelings about a risk

This formulation suggests the following sources of biased perceptions of risk:

1. The inaccurate identification of symptoms or behaviors (i.e., S ≠ ψ)
2. The inaccurate perceptions of diagnosticity of the symptom or behavior ( β ≠ δ )
3. Inaccurate feelings about a risk ( α ≠ a )

So why do consumers inaccurately identify symptoms/behaviors or inappropri-
ately estimate their diagnosticity? What are the sources of bias that lead to a diver-
gence between S and ψ , between β and δ , and between a and α ? Next, we examine
the sources of bias in a bottom-up process as understanding the antecedents of the
bias provides usable ways to de-bias risk.
One set of factors that is likely to affect perceptions of risk is the frequency and
recency of having engaged in behaviors that increase or decrease risk of a disease,
such as smoking for lung cancer, unsafe sex for sexually transmitted diseases
(STDs), and or vaccinations for Hepatitis-B. Another set of factors are the symp-
toms that are associated with the disease itself.
Underestimation of risk would imply that if consumers were using a bottom-up
process to estimate their risk, then they were not adequately taking into account
their own risk behavior or symptoms while constructing their risk judgment, or
exaggerating the impact of their preventive behaviors. Accordingly, manipulations
that can increase the accessibility of their own risk behaviors or symptoms and
reduce the accessibility of their preventive behaviors should be effective at helping
consumers make less biased risk judgments.
An overestimation of risk, on the other hand, would imply that consumers were
excessively weighting their risk behaviors or symptoms, or underweighting their
preventive behaviors. Accordingly, to de-bias overestimates, manipulations should
be aimed at helping consumers appropriately calibrate the diagnosticity of the
behaviors or symptoms for a disease. Overestimation could also be due to consum-
ers including behaviors or symptoms that are not actually associated with a disease
into their cognitive algebra. The route to de-bias overestimates through this route is
the same; helping consumers recognize that the behavior or symptom is not diag-
nostic of the disease and, accordingly, should not be included in their cognitive
algebra.
Increasing the accuracy of a consumer's assessment of the presence and diagnos-
ticity of a single symptom or behavior will not necessarily increase the accuracy of
their risk estimate (Brewer et al. 2004 ). If a consumer already underestimating risk