Abstract
The identification of relevant attributes is the first objective of conjoint analysis. Today, as a result of technological development, it is common for researchers to use adaptive conjoint analysis (ACA) which combines different types of research (e.g. self-assessment questionnaires with an orthogonal design for experiments). ACA, based on partial profiles, is a flexible sequential model that tailors the experimental design to each respondent depending on their previously stated preferences ordered in the self-assessment questionnaire. However, many authors hold that the full profile offers more advantages than the partial one, because it develops a more realistic description of stimuli. Based on full profiles, this study proposes a new strategy to improve the performance of the second step of the ACA process. This strategy allows for estimations of main factors and two-factor interactions with the lowest number of profiles. Our proposal is based on the use of a full profile approach in which the profiles are arranged in two-level factorial designs in blocks of two, and the levels of each factor are codified in a vector manner.
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Huertas-García, R., Forgas-Coll, S., Gázquez-Abad, J.C. (2012). A Proposal for Improving the Performance of Adaptive Conjoint Analysis. In: Gil-Lafuente, A., Gil-Lafuente, J., Merigó-Lindahl, J. (eds) Soft Computing in Management and Business Economics. Studies in Fuzziness and Soft Computing, vol 286. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30457-6_28
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DOI: https://doi.org/10.1007/978-3-642-30457-6_28
Publisher Name: Springer, Berlin, Heidelberg
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