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Die Bewährung von Ankerpreismodellen bei der Erklärung der Markenwahl

Author

Listed:
  • Harald Hruschka

    (Universität Regensburg)

  • Werner Fettes

    (debis Systemhaus GEI)

  • Markus Probst

    (msg Systeme)

Abstract

Summary We evalute reference price models with regard to their ability to explain brand choices of individual households. Reference price models are of the adaptive expectations and extrapolative expectations types. Brand choice is analyzed by means of multinomial logit (MNL) models. We specify the deterministic utility component of MNL-Models as both conventional linear function and nonlinear function. Nonlinear utility is approximated by an appropriate neural network, a feedforward multilayer perceptron with sigmoid hidden units. Reference price models of the extrapolative expectation type formed by lagged prices and a time trend are superior to those of the adaptive expectation type for household scanner panel data. Improvements of posterior probabilities of choice models due to the inclusion of reference prices, losses and gains are greater if nonlinear utility choice models are used.

Suggested Citation

  • Harald Hruschka & Werner Fettes & Markus Probst, 2002. "Die Bewährung von Ankerpreismodellen bei der Erklärung der Markenwahl," Schmalenbach Journal of Business Research, Springer, vol. 54(5), pages 426-441, August.
    • Handle: RePEc:spr:sjobre:v:54:y:2002:i:5:d:10.1007_bf03376986
    DOI: 10.1007/BF03376986
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    References listed on IDEAS

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    1. Mayhew, Glenn E & Winer, Russell S, 1992. "An Empirical Analysis of Internal and External Reference Prices Using Scanner Data," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(1), pages 62-70, June.
    2. McFadden, Daniel, 1980. "Econometric Models for Probabilistic Choice among Products," The Journal of Business, University of Chicago Press, vol. 53(3), pages 13-29, July.
    3. Briesch, Richard A, et al, 1997. "A Comparative Analysis of Reference Price Models," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(2), pages 202-214, September.
    4. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
    5. Winer, Russell S, 1986. "A Reference Price Model of Brand Choice for Frequently Purchased Products," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(2), pages 250-256, September.
    6. Marcel L. Corstjens & David A. Gautschi, 1983. "Formal Choice Models in Marketing," Marketing Science, INFORMS, vol. 2(1), pages 19-56.
    7. Praveen K. Kopalle & Ambar G. Rao & João L. Assunção, 1996. "Asymmetric Reference Price Effects and Dynamic Pricing Policies," Marketing Science, INFORMS, vol. 15(1), pages 60-85.
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    More about this item

    Keywords

    M31; C25; C45;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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