MétaCan
Menu
Back to cohort
Record W3157892169 · doi:10.1111/1748-8583.12357

Institutional duality and human resource management practice in foreign subsidiaries of multinationals

2021· article· en· W3157892169 on OpenAlexaff
Eleni Stavrou, Emma Parry, Paul N. Gooderham, Michael Morley, Mila Lazarova

Bibliographic record

VenueHuman Resource Management Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSubsidiaryMultinational corporationBusinessContext (archaeology)Competitor analysisHuman resource managementAgency (philosophy)ConformityInstitutional theoryIndustrial organizationMarketingManagementEconomicsSociologyPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

Abstract We examine how institutional context affects the decisions that subsidiaries of multinational corporations (MNCs) make in pursuing particular human resource management (HRM) practices in response to institutional duality. Drawing on Varieties of Capitalism, along with the concept of intermediate conformity, we argue that the use of particular HRM practices by MNC subsidiaries will differ depending on both the combination of home and host institutional contexts, and on the nature of the particular practice under consideration. Using data from a survey of HRM practices in 1196 firms across 10 countries, we compare HRM practices in subsidiaries located and headquartered in different combinations of liberal and/or coordinated market economies. Our study suggests MNC subsidiaries conform only to the most persuasive norms, while exercising their agency to take advantage of the opportunities presented by institutional duality to adopt practices that distinguish them from indigenous competitors.

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)0.004
metaresearch head score (Gemma)0.011
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 score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
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 head0.042
GPT teacher head0.364
Teacher spread0.322 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueHuman Resource Management JournalSame topicInternational Student and Expatriate ChallengesFrench-language works237,207