IDEAS home Printed from https://ideas.repec.org/p/vnm/wpaper/167.html
   My bibliography  Save this paper

Leading advertisers efficiency evaluated by data envelopment analysis

Author

Listed:
  • Andrea Ellero

    () (Department of Applied Mathematics, University of Venice)

  • Stefania Funari

    () (Department of Applied Mathematics, University of Venice)

  • Elena Moretti

    () (Department of Applied Mathematics, University of Venice)

Abstract

In this paper we analyze the problem of measuring the advertising efficiency of the Leading US Advertisers during the period 2001-2006. We use the DEA (Data Envelopment Analysis) approach that enables to evaluate the relative efficiency in case of multiple inputs and outputs. In particular, the classical CCR-DEA model is first implemented in each year considered; a windows analysis approach is then used in order to better capture the dynamics of efficiency. Finally, the effect on efficiency of advertising spending over time, is captured by Adstock as an additional variable of the DEA model. The dynamics of Adstock is described by a finite difference equation.

Suggested Citation

  • Andrea Ellero & Stefania Funari & Elena Moretti, 2008. "Leading advertisers efficiency evaluated by data envelopment analysis," Working Papers 167, Department of Applied Mathematics, Universit√† Ca' Foscari Venezia.
  • Handle: RePEc:vnm:wpaper:167
    as

    Download full text from publisher

    File URL: http://virgo.unive.it/wpideas/storage/2008wp167.pdf
    File Function: First version, 2008
    Download Restriction: no

    References listed on IDEAS

    as
    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

    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:vnm:wpaper:167. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marco LiCalzi). General contact details of provider: http://edirc.repec.org/data/dmvenit.html .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.