Making Remote Work Feel Like Home: Developing a Sense of Belonging in Remote and Hybrid Workplaces
Bibliographic record
Abstract
The rise of remote and hybrid work has reshaped organizational dynamics, with implications for how employees develop a sense of belonging. The COVID-19 reshaped employment trends, increasing the prevalence of remote work. The number of remote workers rose from 13% in 2020 to 27% by 2022 worldwide (Statista, 2025). The preference for remote work is reflected in global surveys, where 71% of workers expressed a desire for a fully remote work (Statista, 2024). Currently, around 27% of the U.S. workforce continues to work remotely, with over 70% of them preferring remote work over returning to office (Statista, 2024). While remote work offers flexibility, it results in isolation and diminished interpersonal connections, undermining psychological well-being, and commitment (Buffer, 2022). The need to belong has been recognized as a fundamental human drive (Baumeister & Leary, 1995), but developing a sense of belonging in remote work contexts is far more complex than simply avoiding exclusion. This research aims to uncover the mechanisms through which remote and hybrid employees develop a sense of belonging, emphasizing the roles of individual and social factors. The goal is to provide insights that help managers facilitate belonging among remote employees by understanding how they form connections within organizations. Using a qualitative approach, 17 semi-structured interviews were conducted. Initial findings highlight the role of leadership presence, personalized onboarding, and opportunities for social interaction in fostering belonging. Actions such as virtual coffee chats, culturally inclusive onboarding activities, and team-building events were identified as key strategies for meaningful connections and inclusivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".