How Work From Home Affects Collaboration: A Large-Scale Study of Information Workers in a Natural Experiment During COVID-19
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Abstract (arXiv)
The COVID-19 pandemic has had a wide-ranging impact on information workers such as higher stress levels, increased workloads, new workstreams, and more caregiving responsibilities during lockdown. COVID-19 also caused the overwhelming majority of information workers to rapidly shift to working from home (WFH). The central question this work addresses is: can we isolate the effects of WFH on information workers' collaboration activities from all other factors, especially the other effects of COVID-19? This is important because in the future, WFH will likely to be more common than it was prior to the pandemic. We use difference-in-differences (DiD), a causal identification strategy commonly used in the social sciences, to control for unobserved confounding factors and estimate the causal effect of WFH. Our analysis relies on measuring the difference in changes between those who WFH prior to COVID-19 and those who did not. Our preliminary results suggest that on average, people spent more time on collaboration in April (Post WFH mandate) than in February (Pre WFH mandate), but this is primarily due to factors other than WFH, such as lockdowns during the pandemic. The change attributable to WFH specifically is in the opposite direction: less time on collaboration and more focus time. This reversal shows the importance of using causal inference: a simple analysis would have resulted in the wrong conclusion. We further find that the effect of WFH is moderated by individual remote collaboration experience prior to WFH. Meanwhile, the medium for collaboration has also shifted due to WFH: instant messages were used more, whereas scheduled meetings were used less. We discuss design implications -- how future WFH may affect focused work, collaborative work, and creative work.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Jul 2020.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 4; 2024: 1; 2025: 1; 2026: 1.
9 papers.
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- 2023Leveraging Large Language Models for Collective Decision-Makingcited by 6
- 2021Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemiccited by 3
- 2023Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemiccited by 3
- 2023How Data Scientists Review the Scholarly Literaturecited by 1
- 2024Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithmcited by 1
- 2026Digital Fragmentation and Generative AI Use Across 103 Million Application Eventscited by 0
- 2026Digital Fragmentation and Generative AI Use Across 103 Million Application Events7 Jul 2026
- 2025Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop28 Aug 2025
- 2024Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm6 Sep 2024
- 2023Leveraging Large Language Models for Collective Decision-Making3 Nov 2023
- 2023Can Workers Meaningfully Consent to Workplace Wellbeing Technologies?13 Mar 2023
- 2023Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemic29 Jan 2023
- 2023How Data Scientists Review the Scholarly Literature10 Jan 2023
- 2021Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US29 May 2021
- 2021Dynamic Silos: Increased Modularity in Intra-organizational Communication Networks during the Covid-19 Pandemic1 Apr 2021