IDEAS home Printed from https://ideas.repec.org/a/eee/worpat/v33y2011i1p42-50.html
   My bibliography  Save this article

A new, innovative and marketable IP diagnosis to evaluate, qualify and find insights for the development of SMEs IP practices and use, based on the AIDA approach

Author

Listed:
  • Petit, Cécile
  • Dubois, Cyrille
  • Harand, Aurore
  • Quazzotti, Serge

Abstract

As in our knowledge economy, intellectual assets represented by intellectual and industrial property and associated rights are of growing importance for companies, it is essential for them to be aware of what they own, how to manage these assets, what are their strengths and weaknesses related to those issues. This is why, through a European project co-financed by the European Commission, an action aimed at solving such issues has been taken. The first part of the work undertaken by this action is presented in this article. It consists in the development of an IP questionnaire, based on the adaptation of the marketing and sales AIDA model, allowing classifying IP practices and uses within a progressive scale. In order to get rapid understanding from companies, graphics that can be easily analyzed have been introduced. The originality of the methodology is that the AIDA classification used, gives the opportunity to classify a set of tools or services to be delivered in order to increase the IP practices and performance in companies.

Suggested Citation

  • Petit, Cécile & Dubois, Cyrille & Harand, Aurore & Quazzotti, Serge, 2011. "A new, innovative and marketable IP diagnosis to evaluate, qualify and find insights for the development of SMEs IP practices and use, based on the AIDA approach," World Patent Information, Elsevier, vol. 33(1), pages 42-50, March.
    • Handle: RePEc:eee:worpat:v:33:y:2011:i:1:p:42-50
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0172-2190(10)00009-8
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Sharifi, Mehdi & Khazaei Pool, Javad & Jalilvand, Mohammad Reza & Tabaeeian, Reihaneh Alsadat & Ghanbarpour Jooybari, Mohsen, 2019. "Forecasting of advertising effectiveness for renewable energy technologies: A neural network analysis," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 154-161.
    2. Carlos Abraham Moya & Daniel Galvez & Laurent Muller & Mauricio Camargo, 2018. "A new framework to support Lean Six Sigma deployment in SMEs," Post-Print hal-01959521, HAL.

    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:eee:worpat:v:33:y:2011:i:1:p:42-50. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/654/description#description .

    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.