Over the past decade, the rapid expansion of digital labor platforms has fundamentally
restructured how work is organized and experienced, with nearly 40 million American
adults having earned income through app-based gig work such as ride-hailing, food de-
livery, and freelance task platforms (Pew Research Center, 2021). Central to this trans-
formation is the rise of algorithmic management, a system in which automated deci-
sion-making tools recruit, schedule, evaluate, and discipline workers in ways that were
historically reserved for human supervisors. Although a substantial body of literature
has documented the flexibility and income opportunities associated with platform
work, comparatively little empirical attention has been devoted to understanding how
algorithmic management shapes gig workers' perceived autonomy and psychological
well-being over extended periods of platform engagement. This dissertation addresses
that gap by investigating the relationship between algorithmic management practices
and worker autonomy, job satisfaction, and mental health across three platform sectors
— ride-hailing, food delivery, and online freelance work. Drawing on a sequential
mixed-methods design that combines a longitudinal survey of 1,240 platform workers
with 42 in-depth interviews conducted across four U.S. metropolitan areas, the study
asks whether the transparency, predictability, and perceived fairness of algorithmic
systems mediate the effects of platform work on worker well-being. The findings carry
significant implications for labor policy, platform governance, and the ethical design of
workplace technologies, offering evidence-based guidance for regulators and platform
companies seeking to balance operational efficiency with the protection of worker
welfare.
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