Adaptive Self-Explication of Multi-Attribute Preferences

In this research we propose a web-based adaptive self-explicated approach for multi-attribute preference measurement (conjoint analysis) with a large number (ten or more) of attributes. Our approach overcomes some of the limitations of previous self-explicated approaches. We developed a computer-based self-explicated approach that breaks down the attribute importance question into a sequence of constant-sum paired comparison questions. We first used a fixed design in which the set of questions is chosen from a balanced orthogonal design and then extend it to an adaptive design in which the questions are chosen adaptively for each respondent to maximize the information elicited from each paired comparison question. Unlike the traditional self-explicated approach, the proposed approach provides standard errors for attribute importance. In a study involving digital cameras described on twelve attributes, we find that the predictive validity (correctly predicted top choices) of the proposed adaptive approach is 35%-52% higher than that of Adaptive Conjoint Analysis, the Fast Polyhedral approach, and the traditional self-explicated approach, irrespective of whether the part-worths were estimated using classical or hierarchical Bayes estimation