The Impacts of Organizational Changes on Work Engagement and Quiet Quitting
Bibliographic record
Abstract
Change has become a constant theme in the world where all organizations face new challenges and opportunities that require them to constantly adapt and evolve. The COVID-19 pandemic highlighted the importance of organizational agility and adaptability in the face of challenges and uncertainties. A recent Gallup poll revealed that most workers in the U.S. workforce are either quiet quitting or highly disengaged. This study aims to investigate the relationship between various organizational changes and the likelihood of work engagement, quiet quitting, and high disengagement. Drawing on a survey on 252 employees in various companies, we find that organizational changes including higher demand for competence, improved results monitoring, enhanced informal communication, and job redesign increased the likelihood of work engagement relative to quiet quitting and high disengagement. Furthermore, organizational and job characteristics such as perceived organizational support and job autonomy moderated the relationship between organizational changes and the likelihood of work engagement, quiet quitting, and high disengagement. Practical implications and suggestions for future research are discussed.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".