Event analysis: organizational financial performance and downsizing
Bibliographic record
Abstract
While most organizations do not have a systematic approach for announcing layoffs, they offer an explanation of why the layoff occurred in the first place. Through the use of a sample of 178 organizations that announced job cuts from 2005-2011, this research provides a current assessment of downsizing causes and consequences. Specifically, we use psychology/sociology theories to posit that the explanation provided in the downsizing announcement can be categorized into social accounts (excuses, justifications, apologies or denials) and include an assessment of the initial reason for the downsizing as a determinant on the intent and impact of downsizing on organizational financial results. First, we provide a summary of reported financial performance measures by social account. Next, we use paired sample t-tests to examine mean differences in organizational financial performance pre and post downsizing commonly used organizational financial performance measures in accounting and human resources. Following this, we conduct a change assessment over time (event history analysis). The results identify that organizational financial performance pre and post downsizing vary based on social account. An additional contribution of this research is the recognition that choice of metrics to calculate organizational financial performance is a critical factor understanding the relationship between human resource practices such as downsizing and organizational performance measures as antecedents and consequences. Through this evaluation, we provide a comprehensive, inter-disciplinary and current awareness of the downsizing phenomena as part of an intentional organizational activity in the context of the broader organizational existence.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".