Striving to Earn More: A Survey of Work Strategies and Tool Use Among Crowd Workers

By Toni Kaplan, Susumu Saito, Kotaro Hara, Jeffrey P. Bigham. 2018

63 citations recorded by the source corpus.

Earning money is a primary motivation for workers on Amazon Mechanical Turk, but earning a good wage is difficult because work that pays well is not easily identified and can be time-consuming to find. We explored the strategies that both low- and high-earning workers use to find and complete tasks via a survey of 360 workers. Nearly all workers surveyed had earning money as their primary goal, and workers used many of the same tools (browser extensions and scripts) and strategies in an attempt to earn more money, regardless of earning level. However, high-earning workers used more tools, were more involved in worker communities, and more heavily used batch completion strategies. A natural next step is to use automated systems to assist workers with finding and completing tasks. Workers found this idea interesting, but expressed concerns about impact on the quality of their work and whether using automated tools to support them would violate platform rules. We conclude with ideas for future work in supporting workers to earn more and design considerations for such tools.

Subfields: Computer Science Applications, Management Science and Operations Research, Human-Computer Interaction, Social Sciences & Humanities

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Citations in the corpus

CrowdWorkSheets: Accounting for Individual and Collective Identities Underlying Crowdsourced Dataset Annotation (2022, background): “Workers often invest significant labor outside the platform itself to find tasks, relying onweb browsers extensions and participating in crowdwork forums [23, 31].”
Quantifying the Invisible Labor in Crowd Work (2021, background): “” Invisible labor is also present in crowd work and it includes activities such as: the unpaid time workers have to invest in finding work, figuring out on their own how to complete the job at hand, or managing their payments [51, 67].”
Rehumanized Crowdsourcing: A Labeling Framework Addressing Bias and Ethics in Machine Learning (2019, background): “…related to dehumanization effects in crowdsourcing have been observed and addressed in prior works, including underpaid contributors (e.g., [13, 14, 20, 30, 40]), incompatible tasks (e.g., [4, 7, 15–17, 20, 21, 36]), tedious work (e.g., [12, 15, 29, 35]), and power imbalance (e.g., [32, 41]).”
Characterizing the Global Crowd Workforce: A Cross-Country Comparison of Crowdworker Demographics (2018, unknown): “Based on previous research on average earnings on microtask platforms (e.g., Kaplan et al., 2018; Berg, 2015; Ross et al., 2010; Horton and Chilton, 2010; Khanna et al., 2010) and on the first author’s experience as a crowdworker on CrowdFlower, we paid US$0.1 for the task.”
The Expertise Involved in Deciding which HITs are Worth Doing on Amazon Mechanical Turk (2021, methodology): “Crowdworkers on Amazon Mechanical Turk (AMT) – also known as Turkers – depend on the platform as an important source of income [11, 32, 50], with some Turkers referring to the platform as a ‘safety net’ [38].”
Fair Work: Crowd Work Minimum Wage with One Line of Code (2019, background): “However, more recent work has called out wages more visibly as a central matter of importance for workers (Kaplan et al. 2018; Martin et al. 2014; Gray and Suri 2019).”