Article ID: | iaor19971728 |
Country: | United States |
Volume: | 42 |
Issue: | 6 |
Start Page Number: | 850 |
End Page Number: | 867 |
Publication Date: | Jun 1996 |
Journal: | Management Science |
Authors: | Green Paul E., Krieger Abba M. |
Keywords: | decision theory: multiple criteria, statistics: regression |
Increasingly, conjoint analysts are being asked to design and analyze studies involving large numbers of attributes and/or attribute levels. Various types of approaches, including attribute bridging, Adaptive Conjoint Analysis, and hybrid models have been proposed to deal with the problem. This paper describes recent developments in hybrid modeling. Four hybrid models are described and compared in terms of their performance in an industry-based study entailing 15 product attributes. Comparisons are made in terms of internal cross-validation, market share estimates, attribute importances clustering, and its relationship to exogenous background variables. The proposed models are also compared to select models from the transportation science literature. The authors emphasize the point that comparative model performance may strongly depend on the ways in which the models are to be used.