The Impact of Job-Based HR System Differentiation on Firm Performance and Employee Attitudes
Bibliographic record
Abstract
The purpose of this research was to examine the previously untested assumption that firms experience greater returns when they apply different human resource management (HRM) systems across various groups of employees or jobs within the same organization. We conducted two studies to test this assumption: Study 1 examined the antecedents and consequences of HRM system differentiation at the organization-level and Study 2 tested the effects of HRM differentiation on employee attitudes. The results of the first study showed that firms appear to differentiate their HRM investments based on the strategic value of occupation groups, which was further associated with within-firm differentiation in human capital. This differentiation, however, was not associated with firm performance outcomes. The findings of the second study indicated that employees also perceived within-firm HRM system differentiation based on the strategic value of occupation groups. HRM system differentiation was associated with higher turnover intentions and lower organizational citizenship behaviors as mediated by the employees’ perceptions of fairness. The preponderance of evidence from these studies suggests that managers should carefully consider the benefits and costs HRM differentiation before implementing such an architecture. Additional implications for research and practice are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".