IDEAS home Printed from https://ideas.repec.org/p/mag/wpaper/100021.html
   My bibliography  Save this paper

Are there gambling effects in incentive-compatible elicitations of reservation prices? An empirical analysis of the BDM-mechanism

Author

Listed:
  • Holger Müller

    (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

  • Steffen Voigt

    (Faculty of Economics and Management, Otto-von-Guericke University Magdeburg)

Abstract

Pricing research suggests incentive compatible evaluations of separate products in so-called monadic designs when consumers' situation-specific WTP is to be elicited in a monopolistic purchase setting. In our study, the lottery-based BDM-mechanism is applied for measuring subjects' WTP for a fast moving consumer good in binding one-on-one interviews at the point of purchase. In previous studies, the validity of elicited WTP measures is commonly checked within subjects with respect to indicators of face and criterion validity (such as interest in buying, preference ratings, compliance rates). In addition, we observed real purchases of a separate validation sample at the point of purchase, thus checking external validity between subjects. As a result, the BDM-based WTPs reveal a sufficient degree of internal face validity. However, the external validity in terms of a goodness of fit between WTP-based predictions and purchases of the validation sample is significantly reduced. Specifically, we observed a substantial underestimation of shares of non-buyers. Hence, a potential bias is indicated, leading to an overrating of consumers' true WTP in the lottery-based BDM-mechanism in the setting of our survey.

Suggested Citation

  • Holger Müller & Steffen Voigt, 2010. "Are there gambling effects in incentive-compatible elicitations of reservation prices? An empirical analysis of the BDM-mechanism," FEMM Working Papers 100021, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
  • Handle: RePEc:mag:wpaper:100021
    as

    Download full text from publisher

    File URL: http://www.fww.ovgu.de/fww_media/femm/femm_2010/2010_21.pdf
    File Function: First version, 2010
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.

    More about this item

    Keywords

    Pricing; Willingness to Pay (WTP); BDM-mechanism; Validation;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:mag:wpaper:100021. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Guido Henkel (email available below). General contact details of provider: https://edirc.repec.org/data/fwmagde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.