Doctoral Dissertations
Date of Award
8-2024
Degree Type
Dissertation
Degree Name
Doctor of Philosophy
Major
Computer Science
Major Professor
Scott Ruoti
Committee Members
Alex Williams, Anastasia Kuzminykh, Audris Mockus
Abstract
Crowdworkers are drawn to the profession in part due to the flexibility it affords. However, the current design of crowdsourcing platforms limits this flexibility. Therefore, it is important to support the overall flexibility of crowdworkers. Incorporating a variety of device types in the workflow plays an important role in supporting the flexibility of crowdworkers, however each device type requires a tailored workflow. The standard workflow of crowdworkers consists of stages of work such as managing and completing tasks. I hypothesize that different devices will have unique traits for task completion and task management. Therefore in this dissertation, I explore what those traits are. Future work can build upon this research by creating tailored workflows and interfaces to best support each device type. To achieve this, this dissertation introduces four pivotal innovations : (1) understanding traits of task completion on smartphones to support the tailored workflow on smartphones in crowdwork (2) understanding of crowdworkers' current task completion and task management practices and expectations when working on smartphone, tablet, speaker and smartwatch to support the flexibility of crowdworkers on all these devices based on crowdworkers’ work practices and expectations. (3) After a broad understanding of crowdworkers’ practices and expectations across different devices, this thesis identifies the systematic differences among crowdworkers in order to develop customizable support depending on workers' individual need for flexibility in crowdsourcing platforms (4) Finally, this dissertation looks into other popular crowdsourcing platform named Prolific to understand work practices of Prolific workers as well as compare Prolific with Amazon MTurk to gain a comprehensive understanding of the traits that support flexibility in different crowdsourcing environments.
Recommended Citation
Dutta, Senjuti, "Understanding Traits to Support Crowdworkers' Flexibility. " PhD diss., University of Tennessee, 2024.
https://trace.tennessee.edu/utk_graddiss/10450
Comments
This work is based upon research supported by the National Science Foundation under award CNS-2238001.