Do high performance work systems improve individual outcomes? Differential effects of HR practices
Bibliographic record
Abstract
Scholars have recently focused on examining: 1) the effects of human resource management (HRM) systems at the individual level on various employee outcomes, and 2) differential effects of individual HR practices or sub-dimensions of HRM systems. In response, our study extends recent developments by categorizing employee perception of high-performance work systems (HPWS) along three sub-dimensions: ability-, motivation-, and opportunity-enhancing HR practices. Then, we investigate the differential effects of these categories on employee outcome. Regression analyses of individual-level data (n=393) indicate that only ability- and opportunity- enhancing HR practices have significant positive relationships with in-role performance and organizational citizenship behavior (OCB). Most conspicuously, motivation-enhancing HR practices are negatively associated with employee outcomes. Our study suggests that HPWS influences individual performance mainly by improving intrinsic side of motivation, and HR practices specifically designed to enhance employees’ extrinsic motivation may hamper the effects of HPWS from translating into positive individual performance. Thus, we invite further examination that re-think an internal fit assumption of the system’s approach.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".