Causes of Early and Later Organizational Adoption: The Case of Corporate Downsizing
Bibliographic record
Abstract
While the causes of organizational adoption of new practices often vary across social contexts, organizational theories seldom recognize this fact. One of the few contextual theories on adoption views the causes of adoption as varying according to the timing of adoption: Economic causes should govern early adoption and institutional causes should govern later adoption. Tests of this theory generally have focused on gradual adoption among noneconomic organizations. Recognizing the need to expand our understanding of the timing of organizational adoption, I examine rapid adoption among economic organizations. More specifically, I focus on the adoption of downsizing programs among Fortune 100 firms and report that economic and institutional factors have affected downsizing throughout the downsizing era. Interpretation of these findings sheds light on the genesis and continuation of the downsizing era and on the impact that the rise of investor capitalism has had on shifts in the specific causes of early and later downsizings. I conclude by stressing the theoretical and practical utility of investigating how new practices spread across organizations in different contexts.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".