Exploring the socio‐political dynamics of front‐line managers’ HR involvement: A qualitative approach
Bibliographic record
Abstract
Abstract Moving beyond the extant HR implementation research that has often viewed the implementation decisions primarily as front‐line managers' (FLMs) prerogative, this article explores interactive processes involving three key actors: HR managers, senior managers, and FLMs. Drawing on a political lens, the authors find that the way in which FLMs enact HR practices depends on the relative power of the enforcing actors (i.e., HR managers) and the endorsing actors (i.e., senior managers). The study findings reveal that while the enforcers employ a range of influence tactics (e.g., legitimization, pressure, rational persuasion, and consultation) to facilitate strict HR enactment, the endorsers use counter‐influence tactics (e.g., legitimization, assertiveness, and inspirational appeal) in support of deviant HR implementation behaviors. Carefully navigating both sides' influence tactics in light of past involvement experiences, FLMs choose subsequent implementation behaviors accordingly. The paper makes a meaningful contribution to the HR devolution research by delving into relational power dynamics that develop over an early phase of HR implementation. The resulting theoretical framework provides novel insights into the reasons behind FLMs' divergent forms of implementation behaviors. It shows that the ongoing multi‐lateral interactions and political maneuverings involving HR‐related actors trigger distinct HR involvement patterns.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".