Conjoint Analysis and Stimulus Presentation — a Comparison of Alternative Methods

  • Michael Brusch
  • Daniel Baier
  • Antje Treppa
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


The rapid development of the multimedia industry has led to improved possibilities to realistically present new product concepts to potential buyers even before prototypical realizations of the new products are available. Especially in conjoint studies — where product concepts are presented as stimuli with systematically varying features — the usage of pictures, sounds, animations, mock ups or even virtual reality should result in a reduction of respondent’s uncertainty with respect to (w.r.t.) innovative features and (hopefully) to an improved validity of the collected preferential responses. This paper examines differences between three different stimulus presentation methods: verbal, multimedia, and real.


Stimulus Presentation Conjoint Analysis Potential Buyer Product Concept Stimulus Card 
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  1. AGARWAL, M.K., GREEN, P.E. (1991): Adaptive Conjoint Analysis versus Self Explicated Models: Some Empirical Results. International Journal of Research in Marketing, 8, 141–146.CrossRefGoogle Scholar
  2. BAIER, D. (1999): Methoden der Conjointanalyse in der Marktforschungs-und Marketingpraxis. In: Gaul, W., Schader, M. (Eds.): Mathematische Methoden der Wirtschaftswissenschaften. Physica, Heidelberg, 197–206.Google Scholar
  3. BAIER, D., GAUL, W. (1999): Optimal Product Positioning Based on Paired Comparison Data. Journal of Econometrics, 89, Nos. 1–2, 365–392.zbMATHGoogle Scholar
  4. BAIER, D., GAUL, W. (2000): Market Simulation Using a Probabilistic Ideal Vector Model for Conjoint Data. In: Gustafsson, A., Herrmann, A., Huber, F. (Eds.): Conjoint Measurement–Methods and Applications. Springer, Berlin, 97–120.CrossRefGoogle Scholar
  5. ERNST, O., SATTLER, H. (2000): Multimediale versus traditionelle Conjoint-Analysen. Ein empirischer Vergleich alternativer Produktpräsentationsformen. Marketing ZFP, 2, 161–172.Google Scholar
  6. GREEN, P.E. and RAO, V.R. (1971): Conjoint Measurement for Quantifying Judgmental Data. Journal of Marketing Research, 8, 355–363.CrossRefGoogle Scholar
  7. HUBER, J.C., WITTINK, D.R., FIEDLER, J.A., MILLER, R. (1993): The Effectiveness of Alternative Preference Elicitation Procedures in Predicting Choice. Journal of Marketing Research, 30, 105–114.CrossRefGoogle Scholar
  8. KROEBER-RIEL, W. (1993): Bildkommunikation. Imagerystrategien für die Werbung. Vahlen, München.Google Scholar
  9. LOOSCHILDER, G.H., ROSBERGEN, E., VRIENS, M., WITTINK, D.R. (1995): Pictorial Stimuli in Conjoint Analysis–to Support Product Styling Decisions. Journal of the Market Research Society, 37, 17–34.Google Scholar
  10. PAIVIO, A. (1971): Imagery and Verbal Processes. Holt, Rinehart and Winston, New York a.o.Google Scholar
  11. PAIVIO, A. (1978): A Dual Coding Approach to Perception and Cognition. In: Pick, A., Saltzman, E. (Eds.): Modes of Perceiving and Processing Information. Lawrence Erlbaum Associates, Hillsdale, 39–51.Google Scholar
  12. RUGE, H.D. (1988): Die Messung bildhafter Konsumerlebnisse. Physica, Heidelberg.CrossRefGoogle Scholar
  13. SATTLER, H., HENSEL-BORNER, S. and KRüGER, B. (2001): Die Abhängigkeit der Validität von demographischen Probanden-Charakteristika: Neue empirische Befunde. Zeitschrift für Betriebswirtschaft, 7, 771–787.Google Scholar
  14. SCHARF, A., SCHUBERT, B., VOLKMER, H.P. (1996): Conjointanalyse und Multimedia. Planung und Analyse, 26-31.Google Scholar
  15. SCHARF, A., SCHUBERT, B., VOLKMER, H.P. (1997): Konzepttests mittels bildgestützter Choice-Based Conjointanalyse. Planung und Analyse, 5, 24–28.Google Scholar
  16. SILBERER, G. (1995): Marketing mit Multimedia im Überblick. In: Silberer, G. (Eds.): Marketing mit Multimedia. Grundlagen, Anwendungen und Management einer neuen Technologie im Marketing. Schäffer-Poeschel, Stuttgart, 331.Google Scholar
  17. STADIE, E. (1998): Medial gestützte Limit Conjoint-Analyse als Innovationstest für technologische Basisinnovationen. Springer, Münster.Google Scholar
  18. TREPPA, A. (2001): Konzeption eines integrativen Vorgehensmodells zur Unterstützung der Konstruktionsmethodik, Dissertation, BTU Cottbus.Google Scholar
  19. WEISENFELD, U. (1989): Die Einflüsse von Verfahrensvariationen und der Art des Kaufentscheidungsprozesses auf die Reliabilität der Ergebnisse bei der Conjoint Analyse. Duncker & Humblot, Berlin.Google Scholar
  20. WITTINK, D.R., CATTIN, P. (1989): Commercial Use of Conjoint Analysis: An Update. Journal of Marketing, 53, 91–96.CrossRefGoogle Scholar
  21. WITTINK, D.R., VRIENS, M., BURHENNE, W. (1994): Commercial Use of Conjoint Analysis in Europe: Results and Critical Reflections. International Journal of Research in Marketing, 11, (1), 41–52.CrossRefGoogle Scholar
  22. ZIMBARDO, P.G., GERRIG, R.J. (1999): Psychologie. Springer, Berlin.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Michael Brusch
    • 1
  • Daniel Baier
    • 1
  • Antje Treppa
    • 2
  1. 1.Institute of Business Administration and EconomicsBrandenburg University of Technology CottbusCottbusGermany
  2. 2.Institute of Production ResearchBrandenburg University of Technology CottbusCottbusGermany

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