Systems

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.

Data-factor allocation involves interrelated supply, circulation, and utilization functions, yet existing provincial assessments often emphasize aggregate development rather than internal system configuration. Using 18 indicators for 30 Chinese provinces from 2012 to 2023, this study develops a systems-oriented measurement framework that distinguishes overall attainment, subsystem balance, and relative bottlenecks. Entropy weighting and arithmetic–geometric aggregation are combined with regional inequality decomposition and distributional dynamics analysis. The results show that mean balance-adjusted capacity increased from 0.0449 in 2012 to 0.2125 in 2023, while subsystem balance evolved non-monotonically and showed no sustained improvement after 2018. Under the baseline measurement specification, the predominant relative bottleneck shifted from circulation in 2012 to utilization in 2023, with each identified as the weakest subsystem in 23 provinces. Net between-region differences accounted for an average of 69.34% of total inequality, while annual development-state transitions exhibited substantial persistence. These findings highlight the diagnostic value of examining internal functional configurations alongside aggregate capacity and provide a proxy-based framework for differentiated provincial assessment.

Engineering troubleshooting involves making sense of trouble while working toward containment or resolution within the conditions of professional practice. As inquiry and action unfold, engineers’ understanding of the relevant problem may also change. This work analyzes semi-structured interviews with 21 engineers, each with more than ten years of industry experience, across 20 interviews and seven industry designations to examine how troubleshooting problems are formed and transformed in practice. The existing body of literature explains aspects of this work, including ill-structured problem solving, representational and experiential sensemaking, and the sociotechnical organization of engineering activity. We show that these dimensions operate jointly and recursively within professional engineering troubleshooting. Across the episodes, the operative problem changed as engineers revised what observations indicated, which system was under inquiry, what actions were available or warranted, how work had to be coordinated, and what could count as sufficient resolution within the relevant sociotechnical conditions. We argue that adequate descriptions of troubleshooting problem recognition, pursuit, and restructuring require attention to these relationships. For engineering education, the claim connects readiness for engineering troubleshooting in industry to a clear pedagogical goal: students need practice recognizing, framing, and revising their understanding of problems as evidence, representations, and constraints change.