Loneliness at Work: How it Happens and How it Affects Stress, Resilience, Behavior, and Wellbeing
Bibliographic record
Abstract
Loneliness has emerged as a critical issue for organizations with detrimental impacts on employee effectiveness, job attitudes, and wellbeing. With 80% of employees experiencing loneliness at work and a dearth of research on loneliness in work settings, organizational research on this topic is of critical importance to both managers and scholars. The extant literature suffers from several oversights, which this symposium addresses. First, this symposium responds to calls for research on the antecedents of loneliness at work (Firoz et al., 2021; Ozcelik & Barsade, 2018) by investigating employee perceptions, employee behaviors, and organizational factors. The papers specifically highlight the role of signals of dissimilarity, social structure, and identity. Second, the literature suffers from a lack of attention to specific psychological mechanisms explaining the relationship between loneliness on employee outcomes. The papers in this symposium show the role of stress and vulnerability. In doing so, the papers highlight that loneliness, while largely negative, may in certain contexts produce positive social outcomes. Finally, the literature has focused on loneliness only within the bounds work-related inputs and outputs. The papers in this symposium show how aspects of one’s family and community play a mitigating role in limiting the negative effects of loneliness on wellbeing and counterproductive behaviors such as withdrawal. Together, the five papers in this symposium investigate diverse groups of workers in various contexts to provide fresh and needed insights to understand how loneliness emerges in modern organizations and how it impacts stress, resilience, behavior, and overall experiences of wellbeing. Building from Belongingness: Signals, Structures, and Mechanisms Forming Loneliness in Organizations Author: Anthony Silard; Luiss Business School Author: Madison Suzanne LaBella; Marist College Author: Sarah Wright; University of Canterbury Author: Sawyer Wilkins; Virginia Commonwealth University Author: Chris Reina; Virginia Commonwealth University Disconnected and Distressed: Examining Anxiety and Loneliness at Work Author: Julie M. McCarthy; University of Toronto Author: ZhengPeng(Matt) Wang; Author: Talya N. Bauer; Portland State University Author: Berrin Erdogan; Portland State University Author: Emily D. Campion; University of Iowa Author: Selin Kudret; Henley Business School When Loneliness Can Lead to Resilience: An Evolutionary Theory of Loneliness Approach Author: Gihyun (G.) Kim; Bryant University Author: Emily Dunham Heaphy; University of Massachusetts Amherst When Leaders Feel Lonely: Withdrawal Behaviors and the Buffering Role of Family Identity Salience Author: Karolina Wenefrieda Nieberle; Durham University Author: Michelle Hammond; Oakland University Remote but Not Alone: Leveraging Community Embeddedness to Boost Well-Being Author: Nabi Ebrahimi; Author: Tamara Montag-Smit; University of Massachusetts Lowell Author: David Greenway; Author: Sarah Kostanski;
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".