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Evaluating Brand Value A Conjoint Measurement Application for the Automotive Industry

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Conjoint Measurement

Abstract

At first, the automotive manager had only one simple question: “What price premium does the brand value of my models allow me to demand on the market?” In this article, we would like to show how conjoint measurement can be used to find an answer to that question. Conjoint measurement is not the only building block for determining brand value, yet it is the foundation on which the “brand simulation model”, which we will introduce here, is built.

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References

  • Agarwal, M. K. (1988), An Empirical Comparison of Traditional Conjoint and Adaptive Conjoint Analysis, Working paper No. 88–140, School of Management, State University of Ohio at Binghampton.

    Google Scholar 

  • Backhaus, K., Erichson, B., Plinke, W. and Weiber, R. (2000), Multivariate Analysemethoden: Eine anwendungsorientierte Einführung, 9th ed., Berlin.

    Google Scholar 

  • Ben-Akiva, M. and Lerman, S. R. (1989), Discrete Choice Analysis: Theory and Application to Travel Demand, 3rd ed., Cambridge MA, London.

    Google Scholar 

  • Brisoux, J. E. and Laroche, M. (1980), Evoked Set Formation and Composition: An Empirical Investigation Under a Routinized Response Behavior Situation, Advances in Consumer Research, 8, 357–360.

    Google Scholar 

  • Carroll, J. D. and Green, P. E. (1995), Psychometric Methods in Marketing Research: Part I, Conjoint Analysis, Journal of Marketing Research, 32, 385–391.

    Article  Google Scholar 

  • Elrod, T., Louviere, J. J. and Davey, K. S. (1992), An Empirical Comparison of Ratings-Based and Choice-Based Conjoint Models, Journal of Marketing Research, 29, 368–377.

    Article  Google Scholar 

  • Green, P. E., Krieger, A. M. and Agarwal, M. K. (1991), Adaptive Conjoint Analysis: Some Caveats and Suggestions, Journal of Marketing Research, 28, 215–225.

    Article  Google Scholar 

  • Green, P. E. and Rao, V. R. (1971), Conjoint Measurement for Quantifying Judgmental Data, Journal of Marketing Research, 12, 355–363.

    Article  Google Scholar 

  • Green, P. E. and Srinivasan, V. (1990), Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice, Journal of Marketing, 54, 3–19.

    Article  Google Scholar 

  • Herrmann, A., Schmidt-Gallas, D. and Huber, F. (2000), Adaptive Conjoint Analysis: Understanding the Methodology and Assessing reliability and Validity; in: Gustafsson, A.; Herrmann, A.; Huber, F., eds., Conjoint Measurement—Methods and Application, Heidelberg, 253–277.

    Google Scholar 

  • Huber, J., Wittink, D., Fiedler, J. and Miller, R. (1993), The effectiveness of alternative preference elicitation procedures in predicting choice, Journal of Marketing Research, 30, 105–114.

    Article  Google Scholar 

  • Johnson, R. M. (1987), Adaptive Conjoint Analysis, in: Sawtooth Software Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing, ed. Ketchum, ID: Sawtooth Software, Inc., 253–265.

    Google Scholar 

  • Kolvenbach, C., Krieg, S. and Felten, C. (2000), Brand Value Simulation, SKP white paper, Bonn.

    Google Scholar 

  • Louviere, J. J. and Hensher, D. A. (1983), Using Discrete Choice Models with Experimental Design Data to Forecast Consumer Demand for a Unique Cultural Event, Journal of Consumer Research, 10, 348–361.

    Article  Google Scholar 

  • McFadden, D. (1974), Conditional logit analysis of qualitative choice behavior, in: Zarembka, P., ed., Frontiers in Econometrics, London, 105–142.

    Google Scholar 

  • Meckes, R. (1998), Logit—Nachfragefunktionen auf Mikrodatenbasis, Aachen.

    Google Scholar 

  • Sattler, H. and Hensel-Börner, S. (2000), Ein empirischer Validitätsvergleich zwischen der Customized Computerized Conjoint Analysis (CCC), der Adaptive Conjoint Analysis und Self — Explicated — Verfahren, Zeitschrift fir Betriebswirtschaft, 6, 705–727.

    Google Scholar 

  • Urban, G. L.; and Hauser, J. R. (1993), Design and Marketing of New Products, 2nd ed., Englewood Cliffs, NJ.

    Google Scholar 

  • Voeth, M. (2000), Nutzenmessung in der Kaufverhaltensforschung - Die Hierarchische Individualisierte Limit Conjoint-Analyse ( HILCA ), Wiesbaden.

    Google Scholar 

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© 2001 Springer-Verlag Berlin Heidelberg

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Kolvenbach, C., Krieg, S., Felten, C. (2001). Evaluating Brand Value A Conjoint Measurement Application for the Automotive Industry. In: Gustafsson, A., Herrmann, A., Huber, F. (eds) Conjoint Measurement. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-06392-7_19

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  • DOI: https://doi.org/10.1007/978-3-662-06392-7_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-06394-1

  • Online ISBN: 978-3-662-06392-7

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