Open AccessUnleashing the Potential of Crowd Work: The Need for a Post-Taylorism Crowdsourcing Model
Author(s) -
Ioanna Lykourentzou,
Lionel Robert,
PierreJean Barlatier
Publication year - 2021
Publication title -
m@n@gement
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.485
H-Index - 23
ISSN - 1286-4692
DOI - 10.37725/mgmt.v24.8373
Subject(s) - crowdsourcing , creativity , workforce , task (project management) , collective intelligence , teamwork , knowledge management , work (physics) , open innovation , crowds , computer science , scale (ratio) , data science , engineering , world wide web , management , political science , psychology , economics , computer security , social psychology , mechanical engineering , physics , quantum mechanics , law
Paid crowdsourcing connects task requesters to a globalized, skilled workforce that is available 24/7. In doing so, this new labor model promises not only to complete work faster and more efficiently than any previous approach but also to harness the best of our collective capacities. Nevertheless, for almost a decade now, crowdsourcing has been limited to addressing rather straightforward and simple tasks. Large-scale innovation, creativity, and wicked problem-solving are still largely out of the crowd’s reach. In this opinion paper, we argue that existing crowdsourcing practices bear significant resemblance to the management paradigm of Taylorism. Although criticized and often abandoned by modern organizations, Taylorism principles are prevalent in many crowdsourcing platforms, which employ practices such as the forceful decomposition of all tasks regardless of their knowledge nature and the disallowing of worker interactions, which diminish worker motivation and performance. We argue that a shift toward post-Taylorism is necessary to enable the crowd address at scale the complex problems that form the backbone of today’s knowledge economy. Drawing from recent literature, we highlight four design rules that can help make this shift, namely, endorsing social crowd networks, encouraging teamwork, scaffolding ownership of one’s work within the crowd, and leveraging algorithm-guided worker self-coordination.