IDEAS home Printed from https://ideas.repec.org/a/igg/jssmet/v5y2014i2p1-18.html
   My bibliography  Save this article

Creation of the Training-Chart: A Step Forward to Make the Training More Effective

Author

Listed:
  • Neetima Agarwal

    (Jaypee Business School, Noida, India)

  • Vandana Ahuja

    (Area-Marketing, Jaypee Business School, Noida, India)

Abstract

This paper aims to explain that it is vital for any organization to imbue employee skills assessment before tailoring any training program and has further accelerated the genesis of the ‘Training-Chart' which is an indicator of both employee skills and organizational expectations .Exploratory research method is used for this study and Employability Skill Framework is developed using Factor analysis. The Employee skill set is further subjected to K-means cluster analysis where every cluster profile extracted represents the detailed summary of the employees in the cluster, in the context of their expertise in the present jobs. Based on these cluster profiles and their implications, 78 respondents have categorised the utility and essentiality of different skill segments on three different levels of organization. This paper is aimed to provide a holistic approach to make the training activities more effective.

Suggested Citation

  • Neetima Agarwal & Vandana Ahuja, 2014. "Creation of the Training-Chart: A Step Forward to Make the Training More Effective," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 5(2), pages 1-18, April.
  • Handle: RePEc:igg:jssmet:v:5:y:2014:i:2:p:1-18
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijssmet.2014040101
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Jumana Waleed & Ahmad Taher Azar & Saad Albawi & Waleed Khaild Al-Azzawi & Ibraheem Kasim Ibraheem & Ahmed Alkhayyat & Ibrahim A. Hameed & Nashwa Ahmad Kamal, 2022. "An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 13(1), pages 1-16, January.

    More about this item

    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:igg:jssmet:v:5:y:2014:i:2:p:1-18. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.