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Record W4417344478 · doi:10.1002/hrm.70043

Between Consistency and Adaptation: How Middle Managers Shape Compensation System Implementation

2025· article· en· W4417344478 on OpenAlexaff
Aino Tenhiälä, Sven Kepes, Saku Mantere, Johanna Maaniemi

Bibliographic record

VenueHuman Resource Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsMcGill University
FundersAgencia Estatal de InvestigaciónTyösuojelurahastoAcademy of Finland
KeywordsMiddle managementCompensation (psychology)MicrofoundationsAgency (philosophy)Human resource managementProcess (computing)Consistency (knowledge bases)Dimension (graph theory)Executive compensationAttribution

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.803
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.256
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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