Conjoint Designs with Interpolation: An Alternative Approach for Reducing the Number of Conjoint Profiles
Conference paper
Abstract
This research presents a new approach for reducing the number of profiles in a conjoint study. This approach uses interpolation to reduce the number of attribute levels included in a conjoint design, thus permitting one to use conjoint designs involving fewer profiles. Also, the paper presents a new interpolation procedure based on subjective distances for estimating the utilities of attribute levels not included in a conjoint design. The results confirm that the predictive validity of conjoint designs with interpolation and the predictive validity of the new interpolation procedure based on subjective distances are high. The implications of these findings are discussed.
Keywords
Predictive Validity Average Correlation Attribute Level Conjoint Analysis Categorical Attribute
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- Addelman, S. 1962. “Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments.” Technometrics 4(February): 21-46.CrossRefGoogle Scholar
- Akaah, I.P. 1988. “Cluster Analysis Versus Q-Type Factor Analysis as a Disaggregation Method in Hybrid Conjoint Modeling: An Empirical Investigation.” Journalofthe Academy of Marketing Science 19 (Summer): 11-18.CrossRefGoogle Scholar
- Ahmed, S.A. and A. d’Astous. 1993. “Cross-National Evaluation of Made-In Concept Using Multiple Cues.” European Journal of Marketing 27 (7): 39-52.CrossRefGoogle Scholar
- Edwards, A.L. 1957. Techniques of Attitude Scale Construction. NY: Appleton-Century-Crofts Inc.CrossRefGoogle Scholar
- Green, P.E. 1984. “Hybrid Models for Conjoint Analysis: An Expository Review.” Journal of Marketing Research 21 (May): 155-169.CrossRefGoogle Scholar
- Green, P.E., and W.S. DeSarbo. 1978. “Additive Decomposition of Perceptions Data Via Conjoint Analysis.” Journal of Consumer Research 5 (June): 58- 65.CrossRefGoogle Scholar
- Green, P.E., and A. Krieger. 1996. “Individualized Hybrid Models for Conjoint Analysis.” Management Science 42 (June): 850-867.CrossRefGoogle Scholar
- Green, P.E. and V. Srinivasan. 1990. “Conjoint Analysis in Marketing:New evelopments with Implications for Research and Practice.” Journal of Marketing 54(October): 3-19.CrossRefGoogle Scholar
- Hagerty, M.R. 1985. “Improving the Predictive Power of Conjoint Analysis: The Use of Factor Analysis and Cluster Analysis.” Journal of Marketing Research 22 (May 1985): 168-184.CrossRefGoogle Scholar
Copyright information
© Academy of Marketing Science 2015