Is compensation actually strategic: The case of profit sharing
Bibliographic record
Abstract
Based on two panels of longitudinal data drawn from consecutive time periods, this paper examines whether there is any link between an establishment’s business strategy, its human resources strategy, or its compensation strategy and its subsequent adoption of employee profit sharing. Results indicate that establishments pursuing a low-cost business strategy or a high-wage compensation strategy were more likely than other establishments to adopt profit sharing in both time periods. For some time now, companies have been urged to regard their compensation system as a key strategic variable (Gomez-Mejia & Balkin, 1992). To maximize its contribution to organizational success, advocates argue, compensation should be seen as a key variable contributing to the successful realization of a firm’s business and human resources strategies (Gerhart & Rynes, 2003). But is this really occurring? Are firms actually linking their compensation practices to their broader organizational strategies? The purpose of this paper is to shed some light on this question by examining whether the business and human resources strategies of an enterprise show any relationship to the subsequent adoption of employee profit sharing. For many years, advocates have argued that employee profit sharing is a
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".