The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Hybrid working from home improves retention without damaging performance
Open accessAbstract
Working from home has become standard for employees with a university degree. The most common scheme, which has been adopted by around 100 million employees in Europe and North America, is a hybrid schedule, in which individuals spend a mix of days at home and at work each week 1,2. However, the effects of hybrid working on employees and firms have been debated, and some executives argue that it damages productivity, innovation and career development 3–5. Here we ran a six-month randomized control trial investigating the effects of hybrid working from home on 1,612 employees in a Chinese technology company in 2021–2022. We found that hybrid working improved job satisfaction and reduced quit rates by one-third. The reduction in quit rates was significant for non-managers, female employees and those with long commutes. Null equivalence tests showed that hybrid working did not affect performance grades over the next two years of reviews. We found no evidence for a difference in promotions over the next two years overall, or for any major employee subgroup. Finally, null equivalence tests showed that hybrid working had no effect on the lines of code written by computer-engineer employees. We also found that the 395 managers in the experiment revised their surveyed views about the effect of hybrid working on productivity, from a perceived negative effect (−2.6% on average) before the experiment to a perceived positive one (+1.0%) after the experiment. These results indicate that a hybrid schedule with two days a week working from home does not damage performance.
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Cites
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Does Working from Home Work? Evidence from a Chinese Experiment *
Bloom, N., Liang, J., Roberts, J., & Ying, Z. J. (2015) · The Quarterly Journal of Economics · doi:10.1093/qje/qju032 · Cited by 1,991
Self-citationEquivalence Testing for Psychological Research: A Tutorial
Lakens, D., Scheel, A. M., & Isager, P. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245918770963 · Cited by 1,560
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Mas, A., & Pallais, A. (2017) · American Economic Review · doi:10.1257/aer.20161500 · Cited by 827
The effects of remote work on collaboration among information workers
Yang, L., Holtz, D., Jaffe, S., Suri, S., Sinha, S., Weston, J., Joyce, C., Shah, N., Sherman, K., Hecht, B., et al. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01196-4 · Cited by 789 · cited 1×
“This is an important result given the evidence that fully remote working can damage employee development and promotions 14,17,18 .”
Idea Generation and the Quality of the Best Idea
Girotra, K., Terwiesch, C., & Ulrich, K. T. (2010) · Management Science · doi:10.1287/mnsc.1090.1144 · Cited by 751
Why Working from Home Will Stick
Barrero, J. M., Bloom, N., & Davis, S. (2021) · doi:10.3386/w28731 · Cited by 641
Self-citationVirtual communication curbs creative idea generation
Brucks, M. S., & Levav, J. (2022) · Nature · doi:10.1038/s41586-022-04643-y · Cited by 380
Smart Working: Work Flexibility Without Constraints
Angelici, M., & Profeta, P. (2024) · Management Science · doi:10.1287/mnsc.2023.4767 · Cited by 283
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Cited by
Organizational Technology Ladders: Remote Work and Generative AI Adoption
Schubert, G. (2026) · Academy of Management Proceedings · doi:10.5465/amproc.2026.22251abstract · Cited by 5
Remote work offers flexibility, but at what cost to learning opportunities for young workers?
Finch, D., & Cook, C. (2026) · doi:10.64628/aam.dwre3ftaw
Metadata
- Authors
- Bloom, N., Han, R., & Liang, J.
- Venue
- Nature
- Published
- 2024-06-12
- Volume
- 630
- Issue
- 8018
- Pages
- 920-925
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W4399567465
- Cited by
- 303
- References
- 17
- FWCI
- 220.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.