How Different Types of Artificial Intelligence Training Relate to Innovative Behavior Among Logistics Employees: A Cognitive–Affective Pathway Model

Despite substantial investments in artificial intelligence (AI) training, logistics organizations often struggle to translate these investments into measurable improvements in employee performance. Drawing on the extended Technology Acceptance Model (TAM), Cognitive Load Theory, and Distributed Cognition Theory, this study examines how three AI-TPACK training dimensions—Strategic, Generative, and Optimization AI Training—are associated with employees’ innovative behavior through cognitive (Task–Technology Fit, AI Self-Efficacy) and affective (Perceived Technology Attractiveness, AI Usage Intention) mechanisms. Survey data from 245 logistics practitioners (cross-sectional, single-source) were analyzed using confirmatory factor analysis, competing structural models, and bootstrapped indirect effects in a final integrated model. The originally proposed dual-track structure was not supported; the data favored a cross-linked specification in which cognitive beliefs inform attitudes across tracks and attractiveness precedes usage intention. Strategic AI training was associated with innovative behavior indirectly, Generative AI training both directly and indirectly, and Optimization AI training showed no unique association once the other dimensions were controlled. Neither Training Environment Quality nor Supervisor Support Leadership moderated the hypothesized relationships in a robust way, although both showed direct associations with outcomes (Training Environment Quality with Task–Technology Fit, β = 0.288; Supervisor Support with innovative behavior, β = 0.141). These cross-sectional, single-source associations refine the technology acceptance literature for AI training and inform HRD strategies for digital transformation in logistics.