Corporate Platforms to Cooperative DAOs: Understanding the potential and problems of a plurality of digital labor platform archetypes
Morshed Mannan & Simon Pek
What the paper says
Academic, practitioner, and policy interest in digital labor platforms (DLPs)–businesses that use their digital infrastructure to intermediate transactions between workers and clients who need their services–is surging. While these transformational platforms have brought many benefits, there are growing concerns about the harms and entrepreneurial risks they create for workers. As such, there is a growing interest in problematizing the ownership and governance of DLPs. Our paper critically compares three increasingly common archetypes–Corporate DLPs, Cooperative DLPs, and Decentralized Autonomous Organization (DAO) DLPs–to discern their likelihood of addressing or exacerbating platform workers’ exposure to harms and entrepreneurial risks. Our analysis identifies promising new opportunities for those interested in cultivating a digital solidarity economy by highlighting the merits and demerits of different alternatives to Corporate DLPs, as well as promising new hybrids like Cooperative DAO DLPs. Furthermore, it advances our understanding of factors that contribute to DLPs’ being structured in particular ways and how choices about DLPs’ structures, in turn, prompt the evolution of organizational archetypes. • Calls for regulating a plurality of organisational archetypes in the digital economy to mitigate harms and risks to workers. • Evaluates the harms and entrepreneurial risks of workers across 3 archetypes: Corporate DLPs, Cooperative DLPs, and DAO DLPs. • Identifies two variants of DAO DLPs, the Non-Cooperative DAO DLP and the Cooperative DAO DLP. • Furthers our conceptual understanding of the benefits, risks, and harms of the burgeoning digital solidarity economy.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.