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Logistic Regression Approach to Predicting Truck Driver Turnover

Logistic Regression Approach to Predicting Truck Driver Turnover

S. Scott Nadler, John F. Kros
Copyright: © 2014 |Volume: 5 |Issue: 1 |Pages: 18
ISSN: 1947-9573|EISSN: 1947-9581|EISBN13: 9781466652538|DOI: 10.4018/ijal.2014010102
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MLA

Nadler, S. Scott, and John F. Kros. "Logistic Regression Approach to Predicting Truck Driver Turnover." IJAL vol.5, no.1 2014: pp.15-32. http://doi.org/10.4018/ijal.2014010102

APA

Nadler, S. S. & Kros, J. F. (2014). Logistic Regression Approach to Predicting Truck Driver Turnover. International Journal of Applied Logistics (IJAL), 5(1), 15-32. http://doi.org/10.4018/ijal.2014010102

Chicago

Nadler, S. Scott, and John F. Kros. "Logistic Regression Approach to Predicting Truck Driver Turnover," International Journal of Applied Logistics (IJAL) 5, no.1: 15-32. http://doi.org/10.4018/ijal.2014010102

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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.

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