Abstract
Over the past two decades conjoint measurement has been a popular method for measuring customers’ preference structures. Wittink and Cattin (1989) estimate that about 400 commercial applications were carried out per year during the early 1980s. In the 1990s this number probably exceeds 1000. The popularity of conjoint measurement appears to derive, at least in part, from its presumed superiority in validity over simpler, less expensive techniques such as self-explication approaches (Leigh, MacKay and Summers 1984). However, when considered in empirical studies, this superiority frequently has not been found (e.g. Green and Srinivasan 1990; Srinivasan and Park 1997). This issue is of major practical relevance. If, at least in certain situations, conjoint measurement is not clearly superior in validity to self-explicated approaches, it becomes highly questionable whether future applications for measuring customers’ preferences should be done by conjoint measurement, as self-explicated approaches are clear advantageous in terms of time and money effort.
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References
Agarwal, M. K. and Green, P. E. (1991), Adaptive Conjoint Analysis versus Self-Explicated Models: Some Empirical Results, International Journal of Research in Marketing, 8, 141–146.
Akaah, I. P. and Korgaonkar, P. K. (1983), An Empirical Comparison of the Predictive Validity of Self-Explicated, Huber-Hybrid, Traditional Conjoint, and Hybrid Conjoint Models, Journal of Marketing Research, 20, 187–197.
Campbell, D. T. and Stanley, J. C. (1966), Experimental and Quasi-Experimental Designs for Research,Chicago.
Johnson, R. (1987), Adaptive Conjoint Analysis, Sawtooth Software Conference on Perceptual Mapping, 253–256.
Gedenk, K., Hensel-Börner, S. and Sattler, H. (1999), Bandbreitensensitivität von Verfahren zur Präferenzmessung, working paper, University of Jena.
Green, P. E. and Helsen, K. (1989), Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study, Journal of Marketing Research, 26, 346–350.
Green, P. E. and Srinivasan, V. (1978), Conjoint Analysis in Consumer Research: Issues and Outlook, Journal of Consumer Research, 5, 103–123.
Green, P. E. and Srinivasan, V. (1990), Conjoint Analysis in Marketing: New Development with Implications for Research and Practice, Journal of Marketing, 54, 3–19.
Green, P. E., Carmone, F. J and Wind, Y. (1972), Subjective Evaluation Models and Conjoint Measurement, Behavioral Science, 17, 288–299.
Green, P. E., Goldberg, S. M. and Montemayor, M. (1981), A Hybrid Utility Estimation Model for Conjoint Analysis, Journal of Marketing, 45, 33–41.
Green, P. E., Goldberg, S.M. and Wiley, J. B. (1982), A Cross-Validation Test of Hybrid Conjoint Models, Advances in Consumer Research, 10, 147–150.
Green, P. E., Krieger, A. M. and Agarwal, M. (1993), A Cross Validation Test of Four Models Quantifying Multiattributed Preferences, Marketing Letters, 4, 369–380.
Heeler, R. M., Okechuku, C. and Reid, S. (1979), Attribute Importance: Contrasting Measurements, Journal of Marketing Research, 16, 60–63.
Hensel-Börner, S. and Sattler, H. (1999), Validität der Customized Computerized Conjoint Analysis (CCC),working paper, University of Jena.
Huber, G. P., Daneshgar, R. and Ford, D. L. (1971), An Empirical Comparison of Five Utility Models for Predicting Job Preferences, Organizational Behavior and Human Performance, 6, 267–282.
Huber, J. C., Wittink, D. R., Fiedler, J. A. and Miller, R. (1993), The Effectiveness of Alternative Preference Elicitation Procedures in Predicting Choice, Journal of Marketing Research, 17, 53–72.
Leigh, T. W., MacKay, D. B. and Summers, J. O. (1984), Reliability and Validity of Conjoint Analysis and Self-Explicated Weights: A Comparison, Journal of Marketing Research, 21, 456–462.
Nitzsch, R. v. and Weber, M. (1993), The Effect of Attribute Ranges on Weights in Multiattribute Utility Measurements, Management Science, 39, 937–943.
Srinivasan, V. (1988), A Conjunctive-Compensatory Approach to the Self-Explication of Multiattributed Preferences, Decision Sciences, 19, 295–305.
Srinivasan, V. and Park, C. S. (1997), Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement, Journal of Marketing Research, 34, 286–291.
Srinivasan, V. and Wyner, G. A. (1989), CASEMAP: Computer-Assisted Self-Explication of Multiattributed Preferences, in: Henry, W., Menasco, M. and Takada, H., eds., New Product Development and Testing, Lexington, 91–111.
Wittink, D. R. and Cattin, P. (1989), Commercial Use of Conjoint Analysis: An Update, Journal of Marketing, 53, 91–96.
Wright, P. (1975), Consumer Choice Strategies: Simplifying vs. Optimizing, Journal of Marketing Research, 12, 60–67.
Wright, P. and Kriewall, M. A. (1980), State of Mind Effects on the Accuracy with which Utility Functions Predict Marketplace Choice, Journal of Marketing Research, 17, 277–293.
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Sattler, H., Hensel-Börner, S. (2000). A Comparison of Conjoint Measurement with Self-Explicated Approaches. In: Conjoint Measurement. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06395-8_5
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DOI: https://doi.org/10.1007/978-3-662-06395-8_5
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