Selected article for: "comprehensive study and large dataset"

Author: Cao, Hancheng; Lee, Chia-Jung; Iqbal, Shamsi; Czerwinski, Mary; Wong, Priscilla; Rintel, Sean; Hecht, Brent; Teevan, Jaime; Yang, Longqi
Title: Large Scale Analysis of Multitasking Behavior During Remote Meetings
  • Cord-id: 61crp77s
  • Document date: 2021_1_28
  • ID: 61crp77s
    Snippet: Virtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to people's productivity and wellbeing. However, we currently have limited understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset c
    Document: Virtual meetings are critical for remote work because of the need for synchronous collaboration in the absence of in-person interactions. In-meeting multitasking is closely linked to people's productivity and wellbeing. However, we currently have limited understanding of multitasking in remote meetings and its potential impact. In this paper, we present what we believe is the most comprehensive study of remote meeting multitasking behavior through an analysis of a large-scale telemetry dataset collected from February to May 2020 of U.S. Microsoft employees and a 715-person diary study. Our results demonstrate that intrinsic meeting characteristics such as size, length, time, and type, significantly correlate with the extent to which people multitask, and multitasking can lead to both positive and negative outcomes. Our findings suggest important best-practice guidelines for remote meetings (e.g., avoid important meetings in the morning) and design implications for productivity tools (e.g., support positive remote multitasking).

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