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Record W2115752582 · doi:10.1177/001979391306600302

Human Resource Management Practices in the Multinational Company: A Test of System, Societal, and Dominance Effects

2013· article· en· W2115752582 on OpenAlexaffabout
Paul K. Edwards, Rocío Sánchez-Mangas, Olga Tregaskis, Christian Lévesque, Anthony McDonnell, Javier Quintanilla

Bibliographic record

VenueIndustrial and Labor Relations Review · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsHEC Montréal
FundersEconomic and Social Research Council
KeywordsMultinational corporationSubsidiaryDominance (genetics)Human resource managementCapitalismContext (archaeology)BusinessBest practiceEconomic geographyEconomic systemPolitical scienceEconomicsManagementPoliticsGeography

Abstract

fetched live from OpenAlex

Does the use of HRM practices by multinational companies (MNCs) reflect their national origins or are practices similar regardless of context? To the extent that practices are similar, is there any evidence of global best standards? The authors use the system, societal, and dominance framework to address these questions through analysis of 1,100 MNC subsidiaries in Canada, Ireland, Spain, and the United Kingdom. They argue that this framework offers a richer account than alternatives such as varieties of capitalism. The study moves beyond previous research by differentiating between system effects at the global level and dominance effects arising from the diffusion of practices from a dominant economy. It shows that both effects are present, as are some differences at the societal level. Results suggest that MNCs configure their HRM practices in response to all three forces rather than to some uniform global best practices or to their national institutional contexts.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.066
GPT teacher head0.365
Teacher spread0.299 · 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 source (direct Gemma or distilled Codex), 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

Citations47
Published2013
Admission routes2
Has abstractyes

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