Do Changes In High-Performance Work Systems Pay Off?A Longitudinal Investigation of Dynamic Fit
Bibliographic record
Abstract
Using an eight-year longitudinal survey, this study investigates the stability– change paradox in human resource (HR) systems by examining how patterns of change in high-performance work systems (HPWS) relate to innovation and financial performance of organizations. The contingency perspective suggests that such change constitutes beneficial flexibility because changes in aspects of HPWS are required to attain dynamic fit. By contrast, the universalistic perspective and organizational ambidexterity suggest that HPWS provides both efficiency and flexibility, which indicates beneficial stability. An exploratory analysis supports both theoretical perspectives and reveals a positive relationship between two distinct patterns of change in the ability-motivation- opportunity dimensions of HPWS and performance outcomes. Long-run consistency in the ability-enhancing dimension (i.e., training and recruitment systems) with continuous incremental change is positively associated with high performance. Conversely, short-run stability with episodic change in the motivation- and opportunity-enhancing dimensions of HPWS (i.e., compensation and employee involvement systems) is positively related to performance. The findings suggest that organizations can benefit from both stability and flexibility in HR systems by appropriately emphasizing long-run adaptability in the ability dimension and short-run adaptation in the motivation and opportunity dimensions of HPWS.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".