COGNITIVE LOAD AND ALGORITHMIC LITERACY: UPSKILLING GIG WORKERS FOR SMART AND GREEN LOGISTICS CORRIDORS

Nidhi Sehgal, Gunjan Malhotra, Vimi Jhan

Abstract


The rapid digitalization of global logistics, coupled with the transition toward smart and environmentally sustainable logistics corridors, has significantly reshaped the landscape of last-mile delivery. Increasingly, this critical segment of supply chain depends on gig-based workers, often engaged through platforms such as food delivery apps and ride-hailing services. These workers operate within highly digitized ecosystems where algorithmic systems dictate task allocation. While such technologies promise efficiency, they increase workers’ cognitive load and hinder their ability to meaningfully interact with these technologies. Emerging sustainability mandates require gig workers to develop new forms of algorithmic literacy, digital competency, and environmental delivery practices. This paper examines how cognitive load affects gig workers’ ability to adapt to smart and green logistics corridors and proposes a conceptual model explaining how algorithmic literacy mediates the relationship between cognitive load and gig workers' capability for smart and green logistics corridors. Building on insights from research in the gig economy, human–algorithm interaction, sustainable logistics, and cognitive psychology, the paper highlights algorithmic literacy as an essential competency for preparing workers to thrive in future gig logistics systems. The proposed model positions cognitive load as a key barrier to upskilling readiness and highlights the role of algorithmic literacy and capability for smart and green logistics corridors. This study offers theoretical insights and practical recommendations for policymakers, platform companies, and logistics educators seeking to prepare gig workers for an increasingly digital and sustainable logistics environment. 

Keywords


Cognitive Load, Algorithmic Literacy, Upskilling Readiness, Digital Learning Orientation, Smart and Green Logistics Corridor, Perceived Algorithmic Control

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DOI: https://dx.doi.org/10.21622/MARLOG.2026.15.1.111

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The International Maritime Transport and Logistics Journal (MARLOG)

E-ISSN: 2974-3141
P-ISSN: 2974-3133

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