Assessing longitudinal relationships between financial performance and downsizing
Bibliographic record
Abstract
Purpose – The purpose of this paper is to suggest that divergent financial performance triggers different rationales for the decision to downsize (excuses, justifications, apologies or denials) and that organizational financial performance post-downsizing varies based on the initial downsizing rationale. Design/methodology/approach – A mixed methods approach paired content analysis of 178 downsizing announcements from 2005 to 2011 with organizational financial data pre and post-downsizing event. Paired sample t -tests determined mean differences in organizational financial performance pre- and post-downsizing based on six commonly used organizational performance measures (accounting and human resources metrics). Longitudinal performance trends were evaluated using event history analysis. Findings – Organizational experiencing both financial growth and decline engage in downsizing, but organizational financial performance varies based on downsizing rationale. For example, organizations engaging in excuse-based downsizing experienced significant levels of volatility and decline pre-downsizing, but growth post-downsizing. However, organizations engaging in justification-based downsizing experienced financial decline pre-downsizing, but no significant additional decline post-downsizing. Research limitations/implications – Collection of information over multiple business or economic cycles, or categorizing organizations based on industry, organizations size or number of employees may provide additional information on the relationship between downsizing and organizational financial performance. Practical implications – Organizational performance pre- and post-downsizing varies based on downsizing rationale. Additionally, metrics used to evaluate downsizing success or failure should be considered carefully. Originality/value – The authors help explain divergent results in existing research on the relationship between downsizing and organizational financial performance by identifying downsizing as a multi-dimensional event. The study indicates that organizational experience both financial growth and decline engage in downsizing, but rationalize the downsizing differently (according to social accounts).
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".