MétaCan
Menu
Back to cohort
Record W2968855708 · doi:10.1002/gsj.1356

Headquarters‐subsidiary knowledge strategies at the cluster level

2019· article· en· W2968855708 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGlobal Strategy Journal · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of TorontoHEC Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsMultinational corporationTypologyCluster (spacecraft)Construct (python library)Economic geographyLeverage (statistics)Knowledge managementBusinessKnowledge transferMarketingIndustrial organizationSociologyComputer scienceEconomics

Abstract

fetched live from OpenAlex

Abstract Research Summary This article examines how multinational enterprises (MNEs) leverage knowledge across clusters. Based on the geographical sources and the contextuality of knowledge, we construct a typology of four MNE knowledge strategies across space: replicating, scouting, connecting, and integrating, and take into consideration their spatial, industrial, and leadership contexts. A fuzzy‐set qualitative comparative analysis of 49 pairs of headquarters‐subsidiary linkages between Canada and China suggests that replicating strategies occur in cluster‐to‐non‐cluster contexts or in fields with a knowledge gap between the two countries, whereas scouting strategies are typical in non‐cluster‐to‐cluster investments. Connecting and integrating strategies are focused on cluster‐to‐cluster contexts. We also find that while connecting occurs in fields where knowledge is locally bounded, integrating takes place in nonlocally bounded contexts. Finally, scouting and integrating strategies are associated with home nationals as subsidiary leaders. Managerial Summary How do multinational enterprises (MNEs) transfer knowledge over space between clusters and between other locations? To explore this question, we construct a typology of four MNE knowledge strategies (replicating, scouting, connecting, and integrating) and examine the spatial, industrial, and leadership conditions of each. By examining 49 headquarter‐subsidiary linkages between Canada and China through detailed interviews, we find that replicating strategies occur in cluster‐to‐non‐cluster contexts or industries with a knowledge gap between the two countries, whereas scouting strategies are typical in non‐cluster‐to‐cluster investments. Connecting and integrating strategies are focused on cluster‐to‐cluster contexts. We also find that while connecting occurs in fields where knowledge is locally bounded, integrating dominates where this is not the case. Finally, scouting and integrating strategies are associated with home nationals as subsidiary leaders.

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.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.005

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 head0.028
GPT teacher head0.263
Teacher spread0.235 · 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