Digital platforms in the gig economy (e.g., Uber, Amazon Mechanical Turk) increasingly use algorithm-based mechanisms to manage workers. One mechanism is algorithmic replacing through which under-performing workers are automatically excluded from platform activities and substituted with other workers. Although algorithmic replacing is an effective means to assure adequate worker output, it causes algoactivism, which refers to worker resistance to the employed algorithms. Drawing on algorithmic management literature, we examined algoactivism using topic modeling and grounded theory on driver posts from Uberpeople.net to identify and classify algoactivistic practices in ridesharing. Our results reveal eleven algoactivistic practices that can be arranged and depicted in a "Categories of Algoactivistic Practices in Ridesharing" (CAR) model. Overall, our study expands the literature on algorithmic management by shedding light on algoactivism as a crucial worker reaction to algorithmic management and provides practitioners with valuable insights to deal with the phenomenon to ascertain a fair working environment.
Jiang, Jennifer; Adam, Martin; and Benlian, Alexander, "Algoactivistic Practices in Ridesharing - A Topic Modeling & Grounded Theory Approach" (2021). ECIS 2021 Research Papers. 1.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.