Regent University

Background: The rapid diffusion of large language models (LLMs) such as Claude, ChatGPT, Gemini, LLaMA, and Mistral is reshaping logistics and supply chain management by embedding generative intelligence into planning, coordination, and governance processes. While prior studies emphasize algorithmic capability, far less is known about how differences in diffusion pathways shape productivity outcomes, managerial cognition, and institutional control. Methods: This study develops and applies an integrative analytical framework—the AI Diffusion Triad—comprising Productivity, Perspective, and Power. Using comparative qualitative analysis of five leading LLM ecosystems, the study examines how technical architecture, access models, and governance structures influence adoption patterns and operational integration in logistics contexts. Results: The analysis shows that diffusion outcomes depend not only on model performance but on socio-technical alignment between AI systems, human workflows, and institutional governance. Proprietary platforms accelerate productivity through centralized integration but create dependency risks, whereas open-weight ecosystems support localized innovation and broader participation. Differences in interpretability and access significantly shape managerial trust, learning, and decision autonomy across supply chain tiers. Conclusions: Sustainable and inclusive AI adoption in logistics requires balancing operational efficiency with interpretability and equitable governance. The study offers design and policy principles for aligning technological deployment with workforce adaptation and ecosystem resilience and proposes a research agenda focused on diffusion governance rather than algorithmic advancement alone.

This paper introduces the "Digital Sidewalk" as a framework for understanding how informal learning occurs in hybrid digital-physical spaces. Drawing from Jane Jacobs' urban theory, Mitchell Duneier's ethnography, Sidewalk, and contemporary entertainment-education principles, we examine how decentralized learning communities mirror the organic, indirect learning processes traditionally observed in vibrant urban spaces. Through a year-long ethnographic study of Buildspace, a global technology learning community spanning online platforms and in-person gatherings, we identify how indirect communication facilitates media literacy and technical skill development. Our findings reveal three key dimensions that parallel Jacobs' sidewalk framework: Digital Movement (temporal rhythms and cross-platform flows), Digital Characters (informal roles that emerge organically), and Digital Safety (trust mechanisms that enable risk-taking). This research extends recent work on indirect communication in entertainment-education, demonstrating how educational messages embedded within community narratives foster measurable learning outcomes. By applying established urban studies frameworks to emergent digital phenomena, this interdisciplinary approach reveals crucial patterns in how knowledge spreads through decentralized networks, offering practical insights for designing resilient learning communities.