Balancing tensions: Buffering the impact of organisational restructuring and downsizing on employee well‐being
Bibliographic record
Abstract
Abstract This study examines the impact of employee experiences of restructuring and downsizing on well‐being. The job demands‐resources model was used to develop hypotheses related to job demands in the form of work intensity and job resources in the form of consultation. The job demands‐resources model allows for direct incorporation of employee perceptions and does not assume a singular, predetermined consequence of HRM practices. Hypotheses were tested via structural equation modelling on a nationally representative sample of over 5,110 employees from the Republic of Ireland in 2009. The findings indicate that work intensity serves as a conduit through which experiences of restructuring and downsizing negatively impact employee well‐being. Notably, consultation served as a buffer, diminishing the extent of this negative experience. The findings illuminate the complex pathways that shape how restructuring and downsizing are perceived by employees and the consequences for well‐being. We discuss the theoretical and managerial implications of these findings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".