Abstract

The global gig economy has rapidly expanded, with 545 platforms spanning 186 countries, constituting 12% of the labor market. Diverse control models are used by gig platforms to manage workers, specifically contrasting algorithmic control prevalent in North America and Europe with a hybrid model combining algorithmic and human control seen in Asian platforms like Meituan. Despite the technological advancements, many gig workers report negative experiences and poor working conditions under these control frameworks. Existing literature has investigated algorithmic and human control separately, yet the interplay between these control methods remains under-investigated. This research aims to clarify whether human and algorithmic controls are complementary or competitive in managing gig workers and identifies the optimal conditions for each approach. The study will analyze data from interviews with gig workers and online forums to evaluate the efficacy of different control models. The findings will contribute to the control literature and provide practical insights for gig platforms to enhance worker conditions globally, making the research highly relevant to real-world issues in the gig economy and its global impact.

Comments

tpp1225

Download

Share

COinS