Analytics to Make Hybrid Work, Work
Name
tindall-atindall-mba-mgt-2022-thesis.pdf
Description
Thesis PDF
Size
4.84 MB
Format
Adobe PDF
Checksum (MD5)
1273b58067e3c690e054a256a6d9188b
Author(s)
Tindall, Andrew J.
Advisor(s)
Welsch, Roy E.
Boning, Duane S.
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
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