MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Analytics to Make Hybrid Work, Work

Author(s)
Tindall, Andrew J.
Thumbnail
DownloadThesis PDF (4.838Mb)
Advisor
Welsch, Roy E.
Boning, Duane S.
Terms of use
In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/
Metadata
Show full item record
Abstract
Hybrid work is a coordination problem at heart—how frequently and on which days of the week should hybrid employees come into the office? The COVID-19 pandemic accelerated a remote work revolution and caused the hybrid model—where employees split time between in-office and remote work—to become the norm as employees return to the office in 2022 and beyond. The shift to fully remote work during the pandemic highlighted numerous remote work benefits. To name a few, zero commute cost, more focus time and more flexibility. The challenge is that remote collaboration is more difficult and time consuming to orchestrate—potentially decreasing innovation. Acknowledging that remote and in-person work have different, and at many times complementary goals, our study tests whether employee collaboration data can help organizations solve the coordination problem inherent in hybrid work. We find that collaboration data can align work groups to maximize in-person collaboration gains while minimizing the number of days in office per week. We use data to recommend the optimal in-office frequency and find that offices will be 60% under capacity when employees return. Most importantly, we think about offices as networks—the value of being in the office scales non-linearly as users increase. We find that organizations can use collaboration data to model employee networks and appropriately align work communities. Ultimately, we develop a scheduling system that will help stabilize office space demand in 2022 and beyond.
Date issued
2022-05
URI
https://hdl.handle.net/1721.1/146706
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Sloan School of Management
Publisher
Massachusetts Institute of Technology

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.