Short-term pain for long-term gain? A longitudinal meta-analysis of downsizing-financial performance relationships
Bibliographic record
Abstract
Introduction Downsizing, and the mass layoff upheavals that ensue, has been euphemistically referred to as a short-term pain, long-term gain strategy. But is that so? Do its financial outcomes over time justify the short-run harm? And, to what extent has its adoption been driven by economic or social rationales over time? Methods To examine these questions, we conducted the most comprehensive meta-analysis on downsizing-financial performance relationships to date, summarizing a total of 905 effect sizes. Using a new meta-analytic method multi-level longitudinal meta-analysis (MLLMA) we analyze temporal dimensions of these relationships. Results Results for downsizing adoption suggest shifting rationales over time, from a defensive response to decline in the 1980s, to a socially legitimate management convention in the 1990s, and back to a defensive response in the 2000s. Short-run market outcomes mirror these shifting rationales, with more negative reactions to defensive downsizing. Across a diverse range of lead/lag times and moderators, we find many negative and heterogeneous performance outcomes. Most importantly, little long-term gain is found. Discussion Our MLLMA helps to address prior criticisms on the lack of temporality in extant downsizing research, while many equivocal relationships, despite almost 40 years of downsizing research, illustrate that considerable avenues for future research remain.
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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 | 0.041 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.020 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".