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

要旨

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 workflows across devices and the way people physically organize and configure their workspaces to support such activity. Further, this survey documents a historically significant 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 configurations and identify five 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 benefits and challenges people face when their workflow is fragmented across multiple devices. These insights have implications for the design of multi-device experiences that support people's fragmented workflows.

著者
Ye Yuan
Microsoft Research, Redmond, Washington, United States
Nathalie Riche
Microsoft Research, Redmond, Washington, United States
Nicolai Marquardt
Microsoft Research, Redmond, Washington, United States
Molly Jane Nicholas
Microsoft Research, Redmond, Washington, United States
Teddy Seyed
Microsoft Research, Redmond, Washington, United States
Hugo Romat
Microsoft, Redmond, Washington, United States
Bongshin Lee
Microsoft Research, Redmond, Washington, United States
Michel Pahud
Microsoft Research, Redmond, Washington, United States
Jonathan Goldstein
Microsoft Research, Redmond, Washington, United States
Rojin Vishkaie
Arizona State University, Tempe, Arizona, United States
Christian Holz
ETH Zürich, Zurich, Switzerland
Ken Hinckley
Microsoft Research, Redmond, Washington, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517702

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Interaction Schemes and Patterns I

286–287
5 件の発表
2022-05-05 18:00:00
2022-05-05 19:15:00