Understanding Multi-Device Usage Paterns: Physical Device Configurations and Fragmented Workflows


METADATA ONLY
Loading...

Date

2022-04

Publication Type

Conference Paper

ETH Bibliography

yes

Citations

Altmetric
METADATA ONLY

Data

Rights / License

Abstract

To better ground technical (systems) investigation and interaction design of cross-device experiences, we contribute an in-depth survey of existing multi-device practices, including fragmented workfows across devices and the way people physically organize and confgure their workspaces to support such activity. Further, this survey documents a historically signifcant moment of transition to a new future of remote work, an existing trend dramatically accelerated by the abrupt switch to work-from-home (and having to contend with the demands of home-at-work) during the COVID-19 pandemic. We surveyed 97 participants, and collected photographs of home setups and open-ended answers to 50 questions categorized in 5 themes. We characterize the wide range of multi-device physical confgurations and identify fve usage patterns, including: partitioning tasks, integrating multi-device usage, cloning tasks to other devices, expanding tasks and inputs to multiple devices, and migrating between devices. Our analysis also sheds light on the benefts and challenges people face when their workfow is fragmented across multiple devices. These insights have implications for the design of multi-device experiences that support people's fragmented workfows.

Publication status

published

Book title

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Journal / series

Volume

Pages / Article No.

64

Publisher

Association for Computing Machinery

Event

CHI Conference on Human Factors in Computing Systems (CHI 2022)

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

multi-device; cross-device computing; distributed user interfaces

Organisational unit

Notes

Funding

Related publications and datasets