Learning on the Go: Understanding How Gig Economy Workers Learn with Recommendation Algorithms

As gig economy platforms increasingly rely on algorithms to manage workers, understanding how algorithmic recommendations influence worker behavior is critical for optimizing platform design and improving worker welfare. In this paper, we investigate the dynamic interactions between gig workers and platform algorithms, with a particular focus on how workers learn to improve their strategy and performance over time. Using multiple quantitative methods, including two-way fixed-effects regression and multinomial logit modeling, we analyze over one million orders completed by gig workers on a retail delivery platform. Our findings reveal a clear learning curve: workers progressively improving their efficiency and on-time delivery performance with increased experience. We also find that while newcomers heavily rely on algorithmic recommendations for task selection, more experienced workers tend to deviate from these recommendations, developing and employing personalized strategies. This shift suggests that experienced workers may perceive algorithmic recommendations as less beneficial or misaligned with their evolved preferences, highlighting the necessity for adaptive recommendation systems. Our research underscores the importance of designing human-centric recommendation algorithms that accommodate workers' learning trajectories, incorporate their feedback, and offer flexibility to support personalized strategies, ultimately enhancing collaborative dynamics and outcomes for both workers and platforms.