Rule Developing Experimentation: A Systematic Approach to Understand & Engineer the Consumer Mind

Indexed in: Book Citation Index, EBSCO,Social Sciences & Humanities.

Consumers have been increasingly involved in the innovation process in the last few decades, a major driving force of business success. This involvement is critically important for innovation, ...
[view complete introduction]


Detecting Explicit and Implicit Interactions within Rule Developing Experimentation

Pp. 48-71 (24)

DOI: 10.2174/978160805284411201010048

Author(s): Alex Gofman, Howard R. Moskowitz


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.


Conjoint analysis, interactions, synergism, suppression, experimental design, scenario analysis, regression analysis.