Previous qualitative work has documented that platform workers place an immense importance on their reputation due to the use of algorithmic management by online labor platforms. We provide a general experimental method, which can be used across platforms and time, for numerically quantifying the intensity with which platform workers experience reputation system-based algorithmic management. Our method works via an experiment where workers choose between a monetary bonus or a positive review. We demonstrate this method by measuring the value that freelancers assigned to positive feedback on Upwork in June 2020. The median freelancer in our sample valued a single positive review at $\\sim$\\$49 USD. We also find that less experienced freelancers valued a positive review more highly than those with more experience. Qualitative data collected during the experiment indicates that many freelancers considered issues related to reputation system-based algorithmic management while choosing between the monetary reward and the positive review.<\/p>\n","protected":false},"excerpt":{"rendered":"
Previous qualitative work has documented that platform workers place an immense importance on their reputation due to the use of algorithmic management by online labor platforms. We provide a general experimental method, which can be used across platforms and time, for numerically quantifying the intensity with which platform workers experience reputation system-based algorithmic management. 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