AI-Enabled Logistics Analytics, Operational Excellence, and Customer Satisfaction: A Multi-Industry Mediation Study
Main article
Abstract
Artificial intelligence (AI) is reshaping how logistics functions sense demand, allocate resources, and serve customers, yet evidence on how AI-enabled analytics actually translates into customer outcomes remains fragmented across single industries and single links of the value chain. Drawing on the dynamic capabilities perspective, this study models operational excellence as the mechanism through which AI-enabled logistics analytics capability influences customer satisfaction, and tests whether the mechanism holds across industries. Survey data were collected from 512 logistics and operations decision-makers in manufacturing, retail and e-commerce, third-party logistics, and healthcare firms, and analyzed with partial least squares structural equation modeling, bootstrapped mediation tests, and permutation-based multi-group analysis. AI-enabled logistics analytics exerts a strong positive effect on operational excellence (beta = 0.612) and a modest direct effect on customer satisfaction (beta = 0.186), while the indirect path through operational excellence (0.316; 63.0 percent of the total effect) confirms complementary partial mediation. The mediation mechanism replicates in all four industries, although the capability-to-excellence link is strongest among third-party logistics providers and weakest in healthcare. Importance-performance analysis identifies algorithmic capability and analytics talent as the highest-leverage yet weakest-performing dimensions. The study contributes a cross-industry account of why AI investments pay off in customer terms chiefly when they are converted into disciplined operational routines.
