Compromising between information completeness and task simplicity: A comparison of self-explicated, hierarchical information integration, and full-profile conjoint methods

This paper compares hierarchical information integration (HII), full-profile (FP) conjoint and self-explicated (SE) approaches to preference measurement in terms of equality of preference structures, predictive abilities, and task load. HII is a method to accommodate larger numbers of attributes in conjoint tasks by structuring the task in a hierarchical fashion. The three approaches are compared in a residential preference study that involves thirteen attributes. The results confirm that conjoint approaches result in better choice predictions than self-explicated measures. No significant differences in performance are found between FP and HII with this number of attributes though there are indications that HII can outperform FP if a suitable hierarchical structure is selected. Finally, it is found that SE is the most quickly completed task but only if it is the first task that a respondent encounters.