The adoption and success of profit‐sharing plans in strategic business units
Bibliographic record
Abstract
Purpose To provide insights as to the determinants of profit‐sharing plan (PSP) adoption, as well as conditions that underlie their successful implementation. Design/methodology/approach The sample comprises strategic business units (SBUs) within a large financial services organization, some of which voluntarily adopted a PSP while others did not. All sample SBUs face similar economic and market conditions. Through a logit analysis, we identify determinants of PSP adoption. Through longitudinal cross‐sectional design, we assess the impact of PSP adoption on earnings growth, as well as conditions that underlie successful implementations. Findings Larger SBUs as well as SBUs exhibiting superior asset growth are more likely to adopt a PSP than other SBUs. Prior earnings performance is not found to be a determinant of PSP adoption. PSP adoption translates into superior earnings growth, but such impact quickly declines over time. Among PSP adopters, earnings growth following PSP adoption is greater for SBUs that adopt late (late adopters) and for those which had poor prior earnings performance. Research limitations/implications Limited external validity as the analysis is performed within a single North American organization. Practical implications PSPs are found to be an effective performance turnaround tool. In addition, their limited life cycle suggests that continuous reinforcements and communications are needed to maintain effectiveness. Originality/value In contrast to most prior research that uses multi‐industry samples, the paper relies on a unique organizational database that controls for confounding factors and different earnings generation processes. Moreover, the paper provides additional insights as to the conditions for success.
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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 | 0.005 | 0.036 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".