IDEAS home Printed from https://ideas.repec.org/a/igg/jssmet/v2y2011i2p48-64.html
   My bibliography  Save this article

Identification of Attributes of TQM in an Educational Institute: A System Model

Author

Listed:
  • Rajiv Sindwani

    (YMCA University of Science & Technology, India)

  • Vikram Singh

    (YMCA University of Science & Technology, India)

  • Sandeep Grover

    (YMCA University of Science & Technology, India)

Abstract

This paper examines and proposes a list of attributes of Total Quality Management (TQM) in an educational institute, and develops a model for the benefit of researchers and academicians. Even though there have been a large number of papers published related to TQM, none of the papers focused on documenting the attributes of TQM in educational institutes using statistical methods. The paper investigates and lists 42 attributes of TQM in educational institutions. A quantitative study, involving the administration of a survey was conducted. The survey instrument consisted of 42 items and was prepared on the basis of attributes of TQM found during Literature Review. The application of Factor Analysis technique is illustrated for grouping the various attributes into Factors. The results of this study will help in a smoother penetration of TQM programs in educational institutes. The period of study is from 1995-2006. Considering the gamut of publications, TQM implementation has seen a steady growth and appears to be heading towards its maturity level.

Suggested Citation

  • Rajiv Sindwani & Vikram Singh & Sandeep Grover, 2011. "Identification of Attributes of TQM in an Educational Institute: A System Model," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 2(2), pages 48-64, April.
    • Handle: RePEc:igg:jssmet:v:2:y:2011:i:2:p:48-64
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jssmet.2011040103
    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:2:y:2011:i:2:p:48-64. 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.