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Beyond a One-Size-Fits-All Approach: Towards Personalizing Multi-device Setups in Crowdwork

Published: 24 April 2023 Publication History

Abstract

Non-workstation devices provide mobility in the sense that they enable work and play in less constrained environments and configurations. Workers have different needs and preferences in terms of work practices that impact work-life balance. One way to increase mobility is to allow for more choices in both workstation and non-workstation devices. This paper investigates whether workers’ preferences for using different devices are distinct. To understand whether these differences in preference exist, we use data from an exploratory qualitative survey of 150 crowdworkers from Amazon Mechanical Turk. We first identify the aspects that influence crowdworkers’ preferences in using non-workstation devices. Next, our thematic analysis of open-ended data indicates that different workers have different preferences for multi-device configuration. Finally, this leads to a discussion of how people have different preferences which would require different non-workstation devices.

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Published In

cover image ACM Conferences
UbiComp/ISWC '22 Adjunct: Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers
September 2022
538 pages
ISBN:9781450394239
DOI:10.1145/3544793
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 April 2023

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Author Tags

  1. Crowdwork
  2. Human Intelligence Tasks
  3. Personalizing multi-device

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UbiComp/ISWC '22

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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