Unveiling the Inter-Related Preferences of Crowdworkers: Implications for Personalized and Flexible Platform Design
<|reference_start|>Unveiling the Inter-Related Preferences of Crowdworkers: Implications for Personalized and Flexible Platform Design: Crowdsourcing platforms have traditionally been designed with a focus on workstation interfaces, restricting the flexibility that crowdworkers need. Recognizing this limitation and the need for more adaptable platforms, prior research has highlighted the diverse work processes of crowdworkers, influenced by factors such as device type and work stage. However, these variables have largely been studied in isolation. Our study is the first to explore the interconnected variabilities among these factors within the crowdwork community. Through a survey involving 150 Amazon Mechanical Turk crowdworkers, we uncovered three distinct groups characterized by their interrelated variabilities in key work aspects. The largest group exhibits a reliance on traditional devices, showing limited interest in integrating smartphones and tablets into their work routines. The second-largest group also primarily uses traditional devices but expresses a desire for supportive tools and scripts that enhance productivity across all devices, particularly smartphones and tablets. The smallest group actively uses and strongly prefers non-workstation devices, especially smartphones and tablets, for their crowdworking activities. We translate our findings into design insights for platform developers, discussing the implications for creating more personalized, flexible, and efficient crowdsourcing environments. Additionally, we highlight the unique work practices of these crowdworker clusters, offering a contrast to those of more traditional and established worker groups.<|reference_end|>
@article{dutta2024unveiling,
    title={Unveiling the Inter-Related Preferences of Crowdworkers: Implications
  for Personalized and Flexible Platform Design},
    author={Senjuti Dutta, Rhema Linder, Alex C. Williams, Anastasia Kuzminykh,
  Scott Ruoti},
    journal={arXiv preprint arXiv:2409.04658},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04658},
    primaryClass={cs.HC}
}