IDEAS home Printed from https://ideas.repec.org/a/aic/journl/y2008v55p183-189.html
   My bibliography  Save this article

An analysis of brand interdependencies using Artificial Neural Networks

Author

Listed:
  • Marusya Ivanova

    (Marketing Department, Faculty of Management and Marketing, "D. Tsenov Academy of Economics", Svishtov)

Abstract

The purpose of this article is to present the abilities of Artificial Neural Networks in analyzing the existing structure of brand interdependencies compared to DE-MCI model. To achieve this pur-pose a comparative study is done based on POS data used by Cooper and Nakanishi in their monograph. The results suggest that ANN model outperform DE-MCI model with regards to model fit and they offer face valid estimates of self and cross-elasticities. Based on the transformed cross-elasticity estimates, a MDS map is produced. This competitive map is used to identify the existing interdepend-encies among the brands in the market.

Suggested Citation

  • Marusya Ivanova, 2008. "An analysis of brand interdependencies using Artificial Neural Networks," Analele Stiintifice ale Universitatii "Alexandru Ioan Cuza" din Iasi - Stiinte Economice (1954-2015), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 55, pages 183-189, November.
    • Handle: RePEc:aic:journl:y:2008:v:55:p:183-189
    as

    Download full text from publisher

    File URL: http://anale.feaa.uaic.ro/anale/resurse/021_M03_Ivanova.pdf
    Download Restriction: no
    File URL: http://anale.feaa.uaic.ro/anale/ro/Arhiva%202008%20-%20Ivanova/227
    Download Restriction: no
    ---><---

    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:aic:journl:y:2008:v:55:p:183-189. 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: Sireteanu Napoleon-Alexandru (email available below). General contact details of provider: https://edirc.repec.org/data/feaicro.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.