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Modelling Public Risk Perceptions for Lifestyle and Technological Hazards. C. Pattison, D. Hedderley, and L. J. Frewer, Department of Consumer Sciences, Institute of Food Research, Early Gate, Whiteknights Road, Reading, Berks, UK
It has long been known that compared to expert risk
assessors, the public tends to overestimate risks associated with
'technological' hazards e.g. food additives and genetic
engineering and also tend to underestimate risks associated with
'lifestyle' hazards e.g. high-fat diet and smoking. Members of
the public also have a tendency to view themselves as being at
less risk and more knowledgeable about risks than others
(optimistic bias) and thus may ignore risk communications if they
consider them to be aimed at more vulnerable, less knowledgeable
others (Frewer et al., 1994). Several key research needs can be
identified from the seminal research by Fischhoff et al. (1978)
and Slovic et al. (1980). In these studies subjects rated hazards
against characteristics the researchers thought to be important
in perception of risk. However when investigating public
perceptions it is important not to make prior assumptions about
which factors underpin these perceptions. These early studies
revealed some basic dimensions on which the public perceived
differences between a broad range of hazards. The constructs
people associate with different hazards depends on the
comparative context or hazard domain within which the hazard is
presented. Studies looking specifically at food related hazards
have yielded similar factor structures (Sparks and Shepherd,
1994) to those found using a broad range of hazards. However,
food covers a broad range of hazard types including both
lifestyle-related and technology-related hazard types. To fully
understand the constructs people associate with food related
hazards exploratory qualitative work is required to elicit the
constructs associated with these characteristics within the
context of technological and lifestyle hazard domains.
Two separate studies (n=35 in both) were conducted to
determine how subjects characterise and differentiate hazards
within lifestyle and technological domains. Repertory grid
methodology and generalised Procrustes analysis were used.
Content analysis of the interview scripts revealed that, in
general, lifestyle hazards were described mainly in terms of the
context of exposure whereas technological hazards were described
mainly in terms of the consequences of exposure. Thus public
definitions of risk appear to depend on the domain within which
the hazard is embedded. Generalised Procrustes analysis produces
consensus plots of how people differentiate between hazards and
the elicited constructs can be related to these plots. As can be
seen from the results of the lifestyle study (see Fig 1), the
primary axis (41.5%) differentiates non-food related hazards,
seen as 'risky', from food related hazards which are associated
with beneficial effects. Also, compared to the other hazards,
'sunbathing', 'unprotected sex' and 'cholesterol' are associated
with an element of control, so we would predict that, within this
lifestyle hazard domain, these hazards are most prone to an
optimistic bias, and by implication people may be less likely to
take heed of risk communications about these particular hazards.
Looking at how the constructs are spread, we can see that there
is a conical distribution, with beneficial effects being opposed
by 'risky' and other negative constructs. However, a different
pattern emerges with the technological hazards (see Fig. 2),
where the constructs are spread more evenly throughout the map,
and there are a greater number of positive constructs. It may be
the case that for these technological hazards attitudes are less
crystallised and polarised as 'good' or 'bad' with positive
attributes playing a more complex and mediating role in
perceptions. Also, within this technology domain, food related
hazards, associated with 'new' and 'lack of information' are
differentiated from non-food related hazards, associated with
'old' and 'lots of information', in terms of the secondary axis
(21%). Again, we can see that 'electricity', 'petrol use' and
'road transport' are associated with an element of control, so we
would predict that, within the context of a technology hazard
domain these hazards are most prone to optimistic bias and
therefore for these hazards in particular, risk communications
are most likely to be ignored.
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Finally, early studies (Fischhoff et al., 1978; Slovic et al.,
1980) were descriptive, informing us of the most important
underlying factors people use to differentiate between hazards,
but not how these factors determine risk perceptions or interact
with other factors. Risk perceptions are socially constructed,
being influenced by many highly correlated factors including
these hazard characteristics, such as individual differences and
social context. Structural equation modelling techniques, such as
EQS (Bentler, 1995), are required that can model risk perceptions
and confirm the nature of hypothesised causal pathways between
the many factors that predict risk perception. Therefore
conceptual models of public risk perceptions within technological
and lifestyle hazard domains will incorporate the constructs
indicated by these exploratory studies along with these other
factors identified from the risk perception literature. These
models will then be tested in future survey work and be analyzed
using EQS, in order to understand non bivariate relationships
between the factors contributing to risk perceptions.
This work was funded by the Office of Science and Technology.
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