Strategic HR system differentiation between jobs: The effects on firm performance and employee outcomes
Bibliographic record
Abstract
The purpose of this research was to understand whether firms apply different human resource management systems to different occupations within the same organization (HR differentiation) and how the extent to which they do so may influence firm and employee outcomes. We conducted two studies pertaining to these questions. The first study was based on data collected from managers, and the results suggest that firms differentiate their HR investments based on the strategic value of occupations to the firm, which was further associated with the human capital of those occupations. Differentiation in human capital was also associated with firm performance. The second study was based on data obtained from nonmanagement employees. The findings indicated that employees who were recipients of less HR system investment had lower fairness perceptions, which were further associated with higher turnover intentions and lower organizational citizenship behavior. Although the evidence from these studies suggests that firms may realize benefits from strategic HR system differentiation, managers should carefully consider how to balance the effects of differentiation on firm performance and employee well‐being before implementing such systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".