Antecedents and outcomes of enabling HR practices: The paradox of consistency and flexibility
Bibliographic record
Abstract
Abstract Reconciling competing demands for consistent HR implementation and providing individualized supervisor support to employees has always been a challenge in strategic human resource management. Given that there is burgeoning evidence that frontline managers (FLMs) are at the center of HR implementation, we examine how the organization helps FLMs reconcile demands for consistent HR implementation and deliver individualized support to those under their supervision. With the data from 181 FLMs and 311 employees reported to these FLMs, we find that FLMs' perceived enabling HR practices mediate the relationship between high‐performance work systems and FLMs' willingness to be flexible (WTBF). Furthermore, WTBF mediates the relationship between FLMs' perceived enabling HR practices and consistent HR implementation and between FLMs' perceived enabling HR practices and employees' individualized support. Our study offers new insights by highlighting that an effective HR system is not merely improving FLMs' HR competency and knowledge but capturing FLMs' WTBF in carrying on a broad range of HR tasks. Furthermore, our study provides an expanded and novel understanding that FLMs will likely face two opposite HR tasks that coexist and should be dealt with simultaneously as a pair. We then discuss the theoretical and practical implications of our findings and suggest future research directions.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".