IDEAS home Printed from https://ideas.repec.org/a/igg/jal000/v5y2014i1p15-32.html
   My bibliography  Save this article

Logistic Regression Approach to Predicting Truck Driver Turnover

Author

Listed:
  • S. Scott Nadler

    (Department of Marketing and Management, University of Central Arkansas, Conway, AR, USA)

  • John F. Kros

    (Department of Marketing and Supply Chain Management, East Carolina University, Greenville, NC, USA)

Abstract

The purpose of this study is to identify those constructs that lead to driver turnover. The theory of reasoned action (TRA), originating in the social psychology literature is the theoretical approach in this study. Interviews with drivers were conducted using the intercept method to develop a survey instrument. The survey was then administered to drivers at large truck stops. This study makes contributions on two fronts. From a managerial perspective the study results indicate that companies can use a technique such as this model as part of their driver retention efforts in order to create competitive advantage by increasing efficiency and cutting costs. The resulting logistic regression model, based on four factors, accounts for eighty eight percent of the variance and accurately predicts which drivers or driver classes are most at risk of turning over.

Suggested Citation

  • S. Scott Nadler & John F. Kros, 2014. "Logistic Regression Approach to Predicting Truck Driver Turnover," International Journal of Applied Logistics (IJAL), IGI Global, vol. 5(1), pages 15-32, January.
    • Handle: RePEc:igg:jal000:v:5:y:2014:i:1:p:15-32
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijal.2014010102
    Download Restriction: no
    ---><---

    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:jal000:v:5:y:2014:i:1:p:15-32. 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.