• Michael S. Garver,

    1. Central Michigan University
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    • (Ph.D. University of Tennessee) is an associate professor of marketing at Central Michigan University. Dr. Garver stays active with the business community through speaking, consulting, and conducting best practice research. His research interests include using leading edge research methods to conduct marketing and logistics research. He has published articles in the Journal of Business Logistics, Supply Chain Management Review, Industrial Marketing Management, Marketing Research, Marketing Management, Business Horizons, Mid-American Journal of Business, Marketing News, and the Journal of Consumer Satisfaction, Dissatisfaction, and Complaining Behavior.

  • Zachary Williams,

    1. Central Michigan university
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    • (Ph.D. Mississippi State University) is an assistant professor of marketing at Central Michigan University, specializing in logistics and supply chain management. He received a B.S. from Central Michigan University, an M.B.A. from University of Michigan-Dearborn. He has published articles in the International Journal of Physical Distribution and Logistics Management, Marketing Management Journal, and College Student Journal along with numerous Refereed conference proceedings. His research interests include relationships with transportation providers, reverse logistics activities, and security issues in logistics.

  • G. Stephen Taylor

    1. Mississippi State University
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    • (Ph.D. Virginia Polytechnic Institute) is Director of the Franklin Furniture Institute and professor of management specializing in human resource management. He received a B.A. and M.A. in social anthropology from the University of Virginia, and a M.B.A. and Ph.D. in management from Virginia Polytechnic Institute. Much of his research has focused on driver retention for longhaul trucking firms. His work has appeared in the Journal of Business Logistics, Transportation Journal, Academy of Management Journal, and Human Relations. He also served as Senior Vice President and Managing Consultant in the Transportation Services practice at Marsh and McLennan.


Multiple regression analysis assumes that one model or theory is relevant for the entire population, yet research has shown that this assumption is often false and may severely limit valid theory development and testing. Latent class regression analysis overcomes this limitation and allows the researcher to identify and develop regression models that are relevant for different segments within the same population. Latent class regression analysis is introduced and is used to analyze truck drivers' intentions to stay with the same firm. This article demonstrates the advantages of testing logistics theory with latent class regression analysis and provides numerous applications for practitioners.