Employee drivers and on-time delivery: an exploratory study of trade-offs between cost efficiency and logistics service quality under market penetration

Purpose This study aims to examine the strategic and operational trade-offs of private fleet utilization, focusing on its impact on on-time delivery performance and how market penetration strategies moderate this relationship amid economic turbulence and rising transportation costs. Design/methodology/approach Drawing on principal-agent theory and asset ownership frameworks, hypotheses are developed and tested using logistic regression models. The analysis evaluates 19,211 outbound shipments from a major US producer, comparing delivery performance between private fleets (employee drivers) and for-hire carriers. Interaction terms assess two moderators: (1) geographic proximity to existing customer markets and (2) scarcity of reliable for-hire capacity. Robustness checks include probit and semi-nonparametric models. Findings Private fleet use correlates with lower on-time delivery overall, reflecting cost prioritization during market uncertainty. However, this negative relationship diminishes significantly in proximal markets with existing customers and regions lacking for-hire capacity, highlighting private fleets' dual role as cost centers and strategic tools for service differentiation. Practical implications Logistics managers should deploy private fleets strategically: prioritize them for underserved or high-value markets to enhance service quality, while using for-hire carriers in stable regions. Hybrid strategies balancing cost and service goals are essential in volatile environments. Originality/value This is the first large-scale shipment-level study to link private fleets with on-time delivery performance and introduce market penetration as critical moderators. Thus, this study explores asset ownership under an agency lens as a contingent governance choice that embeds a strategic cost-service trade-off rather than a deterministic solution and specifies boundary conditions that yield a theory-grounded when-to-invest logic for elevating logistics service.