Abstract

Digital labor platforms (DLPs) digitally connect human workers with consumers and arrange for payment, for a type of work known as digital platform work. DLPs face challenges due to the complexity of control of digital platform work: controlling a large number of workers, high flexibility of work, and aggravated worker resistance. Accordingly, they execute algorithmic control, which, however, presents problematic scenarios, such as lack of autonomy and fairness, and low worker well-being. Problematizing control executed solely by DLPs and using only algorithms, we address the research question: How are workers in digital platform work controlled by (multiple) algorithmic and human controllers? We conducted a study of digital platform work in one of the world’s largest app-based food delivery DLPs. We investigated different control mechanisms and how they were executed. Based on 28 interviews with workers and managers, our findings indicate three themes—controllers, control functions, and control means. Analyzing the themes, we develop a ‘partitioning model’ for the control of digital platform work. We theorize the distribution of the control of digital platform work between multiple controllers (i.e., DLPs, third-party intermediaries, and customers) and between algorithmic and non-algorithmic control mechanisms. Control partitioning provides a novel theoretical perspective that brings the human back into the loop of control, in contrast to existing studies which suggest DLPs as the only controllers and algorithmic control as the exclusive control means. It furthers the literature by highlighting and addressing the complexity of control of digital platform work and problems associated with its algorithmic control.

DOI

10.17705/1jais.00981

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