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Wie robust sind Methoden zur Präferenzmessung?

  • Präferenzmessung
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Summary

There is empirical evidence that self-explicated preference measurement methods are surprisingly robust in comparison to conjoint analysis. However, there has been no broad comparison of self-explicated methods with Choice-Based Conjoint analysis. The latter method gains more and more importance in marketing research practice. This empirical study shows that choice-based conjoint analysis leads to decisively better predictive validity than self-explicated measurement. Furthermore, its predictive validity is better than that of a new non-compensatory preference measurement method called RSS and the widespread Adaptive Conjoint Analysis (ACA).

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Literaturverzeichnis

  • Addelman, Sidney (1962), Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments, in: Technometrics, Vol. 4, S. 21–46.

    Article  Google Scholar 

  • Agarwal, Manoj K./ Green, Paul E. (1991), Adaptive Conjoint Analysis Versus Self-Explicated Models: Some Empirical Results, in: International Journal of Research in Marketing, Vol. 8, S. 141–146.

    Article  Google Scholar 

  • Akaah, Ishmael P./ Korgaonkar, Pradeep K. (1983), An Empirical Comparison of the Predictive Validity of Self-Explicated, Huber-Hybrid, Traditional Conjoint, and Hybrid Conjoint Models, in: Journal of Marketing Research, Vol. 20, S. 187–197.

    Article  Google Scholar 

  • Albrecht, Jens (2000), Präferenzstrukturmessung: Ein empirischer Vergleich der Conjoint-Analyse mit einer kompositionellen Methode.

  • Andrews, Rick L./ Manrai, Ajay K. (1998), Simulation Experiments in Choice Simplifications: The Effect of Task and Context on Forecasting Performance, in: Journal of Marketing Research, Vol. 35, S. 198–209.

    Article  Google Scholar 

  • Bettman, James R./Johnson, Eric J./Payne, John W. (1991), Consumer Decision Making, in: Robertson, T. S./Kassarjian, H. H. (Hrsg.), Handbook of Consumer Behavior, S. 50–84.

  • Böcker, Franz (1986), Präferenzforschung als Mittel marktorientierter Unternehmensführung, in: zfbf, 38. Jg., S. 543–574.

    Google Scholar 

  • Bretton-Clark (1989), CONJOINT LINMAP User manual.

  • Dietl, Helmut/ Franck, Egon (2000), Free-TV, Abo-TV, Pay per View-TV — Organisationsformen zur Vermarktung von Unterhaltung, in: zfbf, 52. Jg., S. 592–603.

    Google Scholar 

  • Diller, Hermann (2000), Preispolitik, 3. Aufl.

  • Einhorn, Hillel J./Hogarth, Robin M. (1988), Behavioral Decision Theory: Processes of Judgment and Choice, in: Bell, D. E./Raiffa, H./Tversky, A. (Hrsg.): Decision making. Descriptive, normative, and prescriptive interactions, S. 113–146.

  • Elrod, Terry/ Louviere, Jordan J./ Davey, Krishnakumar S. (1992), An Empirical Comparison of Ratings-Based and Choice-Based Conjoint Models, in: Journal of Marketing Research, Vol. 29, S. 368–377.

    Article  Google Scholar 

  • Ernst, Olaf (2001), Multimediale versus abstrakte Produktpräsentationsformen bei der Adaptiven Conjoint-Analyse: Ein empirischer Validitätsvergleich.

  • Green, Paul E./ Helsen, Kristiaan (1989), Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study, in: Journal of Marketing Research, Vol. 36, S. 346–350.

    Article  Google Scholar 

  • Green, Paul E./ Krieger, Abba M./ Agarwal, Manoj K. (1993), A Cross Validation Test of Four Models for Quantifying Multiattribute Preference, in: Marketing Letters, Vol. 4, S. 369–380.

    Article  Google Scholar 

  • Green, Paul E./ Srinivasan, V. (1978), Conjoint Analysis in Consumer Research: Issues and Outlook, in: Journal of Consumer Research, Vol. 5, S. 103–122.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Gustafsson, Anders/Herrmann, Andreas/Huber, Frank (2001), Conjoint Analysis as an Instrument of Market Research Practice, in: Gustafsson, A./Herrmann, A./Huber, F. (Hrsg.), Conjoint Measurement: Methods and Applications, 2. Aufl., S. 5–46.

  • Haaijer, Rinus/Wedel, Michel (2001), Conjoint Choice Experiments: General Characteristics and Alternative Model Specifications, in: Gustafsson, A./Herrmann, A./Huber, F. (Hrsg.), Conjoint Measurement: Methods and Applications, 2. Aufl., S. 319–360.

  • Hartmann, Adriane/ Sattler, Henrik (2002), Preference Measurement in Marketing Research Practice, Arbeitspapier, Universität Hamburg, Hamburg.

    Google Scholar 

  • Hensel-Börner, Susanne/ Sattler, Henrik (2000), Ein empirischer Validitätsvergleich zwischen der Customized Computerized Conjoint Analysis (CCC), der Adaptive Conjoint Analysis und Self-Explicated Verfahren, in: Zeitschrift für Betriebswirtschaft, 70. Jg., S. 705–727.

    Google Scholar 

  • Herrmann, Andreas/Schmidt-Gallas, Dirk/Huber, Frank (2001), Adaptive Conjoint Analysis: Understanding the Methodology and Assessing Reliability and Validity, in: Gustafsson, A./Herrmann, A./Huber, F. (Hrsg.), Conjoint Measurement: Methods and Applications, 2. Aufl., S. 253–278.

  • Huber, Joel (1991), The Importance of Multinomial Logit Analysis of Individual Consumers Choices, in: Conference Proceedings of First Annual Advanced Research Technique Forum, American Marketing Association, S. 130–142.

  • Huber, Joel (1997), What We Have Learned form 20 Years of Conjoint Research: When to use Self-Explicated, Graded Pairs, Full Profiles or Choice Experiments, in: Conference Proceedings of the Sawtooth Software Conference, Seattle, WA.

  • Huber, Joel/Wittink, Dick R./ Fiedler, John A./ Miller, Richard (1993), The Effectiveness of Alternative Preference Elicitation Procedures in Predicting Choice, in: Journal of Marketing Research, Vol. 30, S. 105–114.

    Article  Google Scholar 

  • Johnson, Richard M. (1987), Adaptive Conjoint Analysis, in: Conference Proceedings of Sawtooth Software Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing, S. 253–265.

  • Johnson, Richard M. (2000), Monotonicity Constraints in Choice-Based Conjoint with Hierarchical Bayes, Arbeitspapier, Sawtooth Software, Inc.

  • Keller, Gerald/Warrack, Brian/Bartel, Henry (1990), Statistics for Management and Economics, 2. Aufl.

  • Klein, Noreen M./ Bither, Stewart W. (1987), An Investigation of Utility-Directed Cutoff Selection, in: Journal of Consumer Research, Vol. 14, S. 240–256.

    Article  Google Scholar 

  • Leigh, Thomas W./ MacKay, David B./ Summers, John O. (1984), Reliability and Validity of Conjoint Analysis and Self-Explicated Weights: A Comparison, in: Journal of Marketing Research, Vol. 26, S. 456–462.

    Article  Google Scholar 

  • Lenk, Peter J./DeSarbo, Wayne S./ Green, Paul E./ Young, Martin R. (1996), Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs, in: Marketing Science, Vol. 15, S. 173–191.

    Article  Google Scholar 

  • Louviere, Jordan J./ Woodworth, George (1983), Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data, in: Journal of Marketing Research, Vol. 20, S. 350–367.

    Article  Google Scholar 

  • Moore, William L./ Gray-Lee, Jason/ Louviere, Jordan J. (1998), A Cross-Validity Comparison of Conjoint Analysis and Choice Models at Different Levels of Aggregation, in: Marketing Letters, Vol. 9, S. 195–207.

    Article  Google Scholar 

  • Orme, Bryan: (2000a), The CBC/HB Module for Hierarchical Bayes Estimation.

  • Orme, Bryan (2000b), Choice-Based Conjoint 2.6, Handbuch.

  • Orme, Bryan (o.J.), ACA, CBC, or both? Effective Strategies for Conjoint Research, Arbeitspapier, Sawtooth Software, Inc.

  • Orme, Bryan/ Baker, Gary (2000), Comparing Hierarchical Bayes Draws and Randomized First Choice for Conjoint Simulations, Arbeitspapier, Sawtooth Software, Inc.

  • Parkinson, Thomas L./ Reilly, Michael (1979), An Information Processing Approach to Evoked Set Formation, in: Advances in Consumer Research, Vol. 6, S. 227–231.

    Google Scholar 

  • Pras, Bernard/ Summers, John (1975), A Comparison of Linear and Nonlinear Evaluation Process Models, in: Journal of Marketing Research, Vol. 12, S. 276–281.

    Article  Google Scholar 

  • Pullman, Madeleine E./ Dodson, Kimberly J./ Moore, William L. (1999), A comparison of conjoint methods when there are many attributes, in: Marketing Letters, Vol. 10, S. 1–14.

    Article  Google Scholar 

  • Sattler, Henrik/Hensel-Börner, Susanne (2001), A Comparison of Conjoint Measurement with Self-Explicated Approaches, in: Gustafsson, A./Herrmann, A./Huber, F. (Hrsg.), Conjoint Measurement: Methods and Applications, 2. Aufl., S. 121–134.

  • Sattler, Henrik/ Hensel-Börner, Susanne/ Krüger, Beate (2001), Die Abhängigkeit der Validität von Conjoint-Studien von demographischen Probanden-Charakteristika: Neue empirische Befunde, in: Zeitschrift für Betriebswirtschaft, 71. Jg., S. 771–787.

    Google Scholar 

  • Srinivasan, V./ Park, Chan Su (1997), Surprising Robustness of the Self-Explicated Approach to Customer Preference Structure Measurement, in: Journal of Marketing Research, Vol. 34, S. 286–291.

    Article  Google Scholar 

  • Srinivasan, V./ Shocker, Allan D. (1973), Linear Programming Techniques for Multidimensional Analysis of Preferences, in: Psychometrika, Vol. 38, S. 337–369.

    Article  Google Scholar 

  • Tscheulin, D.K./ Blaimont, C. (1993), Die Abhängigkeit der Prognosegüte von Conjoint-Studien von demographischen Probanden-Charakteristika, in: Zeitschrift für Betriebswirtschaft, 63. Jg., S. 839–847.

    Google Scholar 

  • Voeth, Markus (1999), 25 Jahre conjointanalytische Forschung in Deutschland, in: Zeitschrift für Betriebswirtschaft, Ergänzungsheft Innovation und Absatz, S. 153–176.

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

  • Voeth, Markus/ Hahn, Christian (1998), Limit Conjoint-Analyse, in: Marketing ZFP, 20. Jg., S. 119–132.

    Google Scholar 

  • Wittink, Dick R./ Vriens, Marco/ Burhenne, Wim (1994), Commercial Use of Conjoint Analysis in Europe: Results and Critical Reflections, in: International Journal of Research in Marketing, Vol. 11, S. 41–52.

    Article  Google Scholar 

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Authors and Affiliations

  1. Institut für Handel und Marketing, Universität Hamburg, Von-Melle-Park 5, 20146, Hamburg, Deutschland

    Adriane Hartmann (Wissenschaftliche Mitarbeiterin) &  Henrik Sattler (Geschäftsführender Direktor)

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  1. Adriane Hartmann
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  2. Henrik Sattler
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Correspondence to Henrik Sattler.

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Hartmann, A., Sattler, H. Wie robust sind Methoden zur Präferenzmessung?. Schmalenbachs Z betriebswirtsch Forsch 56, 3–22 (2004). https://doi.org/10.1007/BF03372727

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