How workplace support for the <scp>COVID</scp>‐19 pandemic and personality traits affect changes in employees' affective commitment to the organization and job‐related well‐being
Bibliographic record
Abstract
How do organizational responses to environmental disruptions affect employees' job-related well-being? As the COVID-19 pandemic has led to new ways of working, increased health concerns, and added responsibilities, employees are facing important challenges in doing their work that can affect their job-related well-being. This study aims to understand how different types of work support (i.e., perceived organizational support and supervisor accessibility) in response to environmental disruption interact with personality traits (i.e., core self-evaluations and future focus) to influence changes in employees' affective commitment to their organization and in their job-related well-being. We develop a moderated mediation model and test it on data collected from 295 individuals working in the United Kingdom. We find that work support for the COVID-19 pandemic, both perceived organizational support and supervisor accessibility, is associated with more positive changes in employees' job-related well-being and that this effect is mediated by changes in employees' affective commitment to their organization. Furthermore, we find that personality traits moderate the relationships between these two types of support and changes in affective commitment to the organization, with those relationships being more positive for employees with low core self-evaluations and for those with a high future focus.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".