Truck Driver Turnover: A Logistic Regression Approach

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Author(s): S. Scott Nadler (University of Central Arkansas, USA)and John F. Kros (East Carolina University, USA)
Copyright: 2018
Pages: 23
Source title: Contemporary Approaches and Strategies for Applied Logistics
Source Author(s)/Editor(s): Lincoln C. Wood (University of Otago, New Zealand & Curtin University, Australia)
DOI: 10.4018/978-1-5225-5273-4.ch016

Keywords: Business & Management / Business Science Reference / Operations and Service Management / Supply Chain Management

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Abstract

The purpose of this chapter is to identify those constructs that lead to driver turnover and to develop a logistic regression model to assist in predicting driver turnover. Interviews with drivers were conducted with 154 drivers at large truck stops. The theory of reasoned action (TRA), originating in the social psychology literature, is the theoretical approach in this study. This chapter makes contributions in two areas. From a managerial perspective, the study results indicate that companies can use a technique such as logistic regression as part of their driver-retention efforts in order to create competitive advantage by increasing efficiency and cutting costs. The resulting logistic regression model provides a concrete tool for analyzing driver turnover. Based on four factors, the model accounts for 84% of the variance and accurately predicts which drivers or driver classes are most at risk of turning over.

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Truck Driver Turnover: A Logistic Regression Approach

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