The chapter introduces two approaches that identify the nature and
magnitude of interaction between concept elements in a conjoint analysis task. Both
approaches use main effects experimental designs, permuted to create hundreds of new
designs that are isomorphic to the original design structure. In the first approach, the
scenario analysis creates a distinct, mutually exclusive, exhaustive set of subgroups
from concepts based upon the commonality of a specific element, runs a dummy
variable regression within each subgroup and identifies the effect of the different
elements on the dependent variable. When compared across the different subgroups in
the regression analysis, the outcome shows the effect of one element on the impact
values of the other elements. In the second approach, also using regression analysis, this
time to understand the pairwise interactions, the analysis forces in all of the linear terms
(single elements) and then allows significant pairwise combinations to enter if they
contribute significant additional predictability to the model. The two approaches
identify the existence of and then measure the impact of, one element on the
performance of others (scenario) and the unexpected effect of mixing two concept
elements (interaction analysis). We illustrate the approaches with a case history dealing
with communicating the sensory and refreshment benefits of an orange beverage.
Keywords: Conjoint analysis, interactions, synergism, suppression, experimental
design, scenario analysis, regression analysis.
