http://research.library.mun.ca/id/eprint/16384
Bibliographic record
Abstract
In this study, I seek to understand what has led workers to engage in a trend known as Quiet Quitting, where employees continue to perform their work but choose not to go above and beyond the formal obligations of their employment. I propose that this phenomenon can be operationalized as reduction of organizational citizenship behaviors and it exists as a result of return to the pre-pandemic status quo in which employees are no longer allowed to work in a primarily remote work environment. I used three well-known theories to explain the possible ways in which the return to a primarily in-person work environment may have led employees to stop going above and beyond in their obligations: psychological contract breaches, work engagement, and adaptive cost. Using a cross-sectional questionnaire, I gathered data from 251 participants on each of those constructs as well as their beliefs in Quiet Quitting and organizational citizenship behaviors. I analyzed the data using partial least squares structural equation modeling. The results suggest that employees being mandated back to a primarily in-person work environment has a negative relationship with meaningfulness. This, in turn, has a negative relationship on their performance of discretionary behaviors. Also, the results suggest that the adaptive costs associated with the COVID-19 drained workers’ resources such that they were less likely to engage in those behaviors whether they wanted to or not. The COVID-19 pandemic was a largely unprecedented event in modern history and the measures to mitigate its spread brought several changes to how work is performed. This study tries to understand the lasting impacts of those changes and the lessons they bring to managers. It will help advance the scholarship on remote work and how three important organizational behavior theories apply to workers in the post-pandemic world. It will also provide further information to help practitioners make informed decisions on the future of the workplace and how much flexibility to give employees in how they perform their jobs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Not applicable | high |
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.895 | 0.796 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".