Workplace Loneliness: When it Happens, Who Experiences It, and How to Prevent It
Bibliographic record
Abstract
Long before the COVID-19 pandemic, the United States Surgeon General declared loneliness as an epidemic (Murthy, 2017) and specifically identified an individual’s work environment as a context where loneliness can emerge and have dire consequences (Seitz, 2023; McDaid, 2022). Indeed, research has demonstrated that 80% percent of employees experience loneliness at work (Twaronite, 2022) and that it is related to a multitude of negative outcomes, such as emotional exhaustion (Anand & Mishra, 2018) and decreased organizational commitment (Ayazlar & Güzel, 2014), job satisfaction (Wright et al., 2006), performance (Ozcelik & Barsade, 2018), and engagement (Jung et al., 2021). Thus, unfortunately, workplace loneliness is a prevalent and pernicious experience in modern organizations. In light of this crisis, management scholars have begun to examine the outcomes of workplace loneliness but have devoted far less attention to identifying the factors that may cause workplace loneliness, exploring how workplace loneliness may emerge at the team level, and investigating how to curb workplace loneliness. Therefore, this symposium brings together six papers that aid in addressing these gaps in our understanding of workplace loneliness. Together, these papers focus on investigating the experience of workplace loneliness in critical groups, such as those with stigmatized identities, entrepreneurs, and leaders, and invite a discussion of possible solutions to limit workplace loneliness and mitigate its negative consequences in individuals and teams. Work Loneliness: From Diagnosis to Intervention Author: Constance Noonan Hadley; Boston U. Questrom School of Business Author: Sarah Wright; U. of Canterbury Workplace Loneliness: The Role of Political Identity Dissimilarity Author: Jason Williamson; - Loneliness of the Stigmatized: A Theoretical Model Among Employees from Stigmatized Groups Author: Arjun Mitra; California State U., Los Angeles Author: Hakan Ozcelik; California State U. Sacramento Author: Lu Wang; U. of Alberta Promises and Perils: Examining the Paradoxical Nature and Consequences of Entrepreneur Loneliness (WITHDRAWN) Author: Olivier D. Boncoeur; U. of Notre Dame Author: Melanie Prengler; U. of Virginia, Darden School of Business Lonely Teams: Extending the Regulatory Loop Theory to the Team Level Author: Madison Suzanne LaBella; Drexel U. Author: Hongjun Ye; U. of Pittsburgh Author: Lauren D'Innocenzo; Drexel U. Storying Loneliness: The Psychodynamic Construction and Deconstruction of Persistent Loneliness Author: Jennifer Petriglieri; INSEAD Author: Elizabeth Sheprow; Harvard Business School
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".