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oai:dspace.mit.edu:1721.1/1467062022-12-01T03:01:17Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Welsch, Roy E. Boning, Duane S. Tindall, Andrew J. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Sloan School of Management 2022-11-30T19:42:37Z 2022-11-30T19:42:37Z 2022-05 2022-08-25T19:15:50.678Z https://hdl.handle.net/1721.1/146706 0000-0002-9206-783X 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. M.B.A. S.M. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ Analytics to Make Hybrid Work, Work Thesis application/pdf Master Master of Business Administration Master of Science in Electrical Engineering and Computer Science Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f0ef74ea-14ac-456c-be8d-b6f0cd78107b"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Analytics to Make Hybrid Work, Work</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2022-05</PublicationDate> <Authors> <Author> <DisplayName>Tindall, Andrew J.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://rightsstatements.org/page/InC-EDU/1.0/</License> <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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>