Subcontracting Microwork

Morris, Meredith Ringel; Bigham, Jeffrey P.; Brewer, Robin; Bragg, Jonathan; Kulkarni, Anand; Li, Jessie; Savage, Saiph

doi:10.1145/3025453.3025687

Cited by 23 publications

(10 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Still, crowd workers continually provide new reviews of requesters to it. In the future, we are also interested in exploring how peer advice could be used to help crowd workers directly make better wages, or learn how to delegate work for which they lack the skills to complete [60].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Crowd Coach

Chiang

Kasunic

Savage

2018

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

Traditional employment usually provides mechanisms for workers to improve their skills to access better opportunities. However, crowd work platforms like Amazon Mechanical Turk (AMT) generally do not support skill development (i.e., becoming faster and better at work). While researchers have started to tackle this problem, most solutions are dependent on experts or requesters willing to help. However, requesters generally lack the necessary knowledge, and experts are rare and expensive. To further facilitate crowd workers' skill growth, we present Crowd Coach, a system that enables workers to receive peer coaching while on the job. We conduct a field experiment and real world deployment to study Crowd Coach in the wild. Hundreds of workers used Crowd Coach in a variety of tasks, including writing, doing surveys, and labeling images. We find that Crowd Coach enhances workers' speed without sacrificing their work quality, especially in audio transcription tasks. We posit that peer coaching systems hold potential for better supporting crowd workers' skill development while on the job. We finish with design implications from our research.

show abstract

Section: Discussion and Future Workmentioning

confidence: 99%

Crowd Coach

Chiang

Kasunic

Savage

2018

Proc. ACM Hum.-Comput. Interact.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For ill-defined tasks like the end-to-end groundbreaking innovation, which are currently not handled by commercial crowdsourcing platforms (Felin & Zenger, 2014;Kim & Robert, 2019), but which, nonetheless, represent the next frontier of crowdsourcing work, we must envision tools that allow workers to participate and even lead the workflow design process, establish social status, allocate roles, and monitor progress. Scholars have already highlighted the potential of role assignment in crowdsourcing (Valentine et al, 2017) and the emerging reality of subcontracting tasks by crowdworkers to other crowdworkers (Morris et al, 2017;Taylor & Joshi, 2018;Wood et al, 2019, for a review see Kim & Robert, 2019).…”

Section: Leverage a Multilayered Self-coordinationmentioning

confidence: 99%

Unleashing the Potential of Crowd Work: The Need for a Post-Taylorism Crowdsourcing Model

Lykourentzou

Robert

Barlatier

2021

mgmt

View full text Add to dashboard Cite

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.

show abstract

“…The design space for crowdsourcing involving third parties has been only recently emerging. Examples include PledgeWork [20], a system for volunteers to donate their income from crowd work to a third party (a charity), revenue sharing (e.g., [8]), and the review of subcontracting microwork by Morris et al [26]. In the classroom with three parties (i.e., the teacher, crowd workers and students), the requester of feedback (the teacher) may not be the receiver and benefciary of the feedback (in this case, the students).…”

Section: Professionalismmentioning

confidence: 99%