Between Consistency and Adaptation: How Middle Managers Shape Compensation System Implementation
Bibliographic record
Abstract
ABSTRACT The success of a human resource management (HRM) system or subsystem, such as a compensation system, hinges on its implementation—yet the microfoundations of this process remain underexplored. To address this gap, we conducted two studies. Study 1 surveyed middle managers and employees in six organizations to examine their attributions of problems with compensation systems and their perceptions of compensation system effectiveness. We found that both groups identified design problems; managers emphasized administrative problems, whereas employees focused on implementation problems. These differing attributions shaped their views of compensation system effectiveness. To further unpack the challenges middle managers face, we analyzed data from Study 2, a 6‐year long in‐depth case study, exploring how and why middle managers varied in their implementation strategies. We found that middle manager identification with the system and their perceived agency explained their implementation strategies, ranging from championing to compliance, and from appropriation to resignation. Together, the studies reveal persistent tensions between consistency and adaptation in HRM implementation. To address these tension, we introduce the concept of internal flexibility —the capacity of middle managers to adjust formal HRM practices during the implementation process to align them with their work unit's needs—as a critical yet underexplored dimension of HRM effectiveness.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".