Making the Gig Economy Infrastructure Work: Gig Drivers’ Adaptive, Algorithmic, and Social Knowledge Practices

Abstract The smooth functioning of the gig economy relies on gig workers’ local execution of tasks assigned by global platforms like Uber. Within this broader workforce, gig drivers (e.g., rideshare and delivery workers) draw on their local knowledge – information and skills specific to a particular region – to navigate and optimize routes. Building on a substantial body of research on gig work, this study turns attention to how gig drivers develop and deploy knowledge practices within the gig economy infrastructure. To address this, we analyzed 25 semi-structured interviews, revealing how drivers’ knowledge practices enable them to adapt platform systems to local conditions, develop algorithmic-local knowledge by integrating insights about platform behavior and market dynamics, and strategically regulate knowledge exchange with peers. We discuss how such knowledge practices play a critical role in localizing platform work, bridging the gaps between gig platforms’ global standards and local conditions.