Ride or Die: Deadline Infrastructure and Overwork in Platform Economies

• In Event: Organizations, Occupations, and Work Section Roundtables
  In Refereed Roundtable (90 minute): Table 14: Platforms and Participants

Abstract

Overwork is pervasive across digital platforms, yet existing accounts of algorithmic management rarely explain why the extent of overwork varies across platforms. We theorize deadline infrastructure (DI)—a set of time-based rules governing exchange—as a mechanism through which platform design shapes the intensity of work. DI has three components: inter-order pacing, finite order-acceptance windows, and reputation-based access to orders. We argue that DI compresses the time window for sellers’ decisions and prices sellers’ delay at the moment of contract formation, thereby reallocating bargaining power among buyers, sellers, and the platform. Under strong DI, tight inter-order intervals, short order-acceptance windows, and reputation-based access penalties make sellers’ refusal immediately costly, thus shifting sellers’ behavior toward acceptance-by-default and working without genuine breaks. This theory generates empirically testable implications, which for sellers take the form of fewer genuine breaks, earlier acceptance decisions within the window, and greater uptake of lower-surplus orders under stronger DI. Empirically, we examine an extreme case with the strongest DI: ride-hailing work on Didi in China. Drawing on long-term participant observation and interviews, we trace the mechanism and show how nearly negative inter-order intervals, seconds-long order-acceptance windows, and immediate reputation-based access together induce extreme overwork among drivers. Moving beyond the extreme case, we show how platforms with more relaxed DI configurations—across sectors, development stages, and institutional contexts—produce systematically milder forms of overwork. This paper offers a generalizable account of how platforms’ temporal institutional design shapes work-related consequences, together with a comparative and measurable framework for cross-platform analysis.

Authors

• Yan Li, peking university

• Jack Linzhou Xing, Harvard University

• Junyan Jiang, Columbia University