MétaCan
Menu
Back to cohort
Record W1968530305 · doi:10.1057/kmrp.2014.25

The mutual construction of knowledge transfer and shared context in capability development within the networked MNC

2014· article· en· W1968530305 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.

Bibliographic record

VenueKnowledge Management Research & Practice · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsWestern University
Fundersnot available
KeywordsKnowledge managementKnowledge transferMultinational corporationContext (archaeology)SubsidiaryBusinessKnowledge sharingAdaptation (eye)InterdependenceKnowledge flowKnowledge value chainConstructiveKnowledge creationProcess managementOrganizational learningComputer scienceProcess (computing)Marketing

Abstract

fetched live from OpenAlex

Consistent with the knowledge-based view of the firm, capability augmentation in the multinational corporation (MNC) entails the adaptation and diffusion of knowledge within a network of globally dispersed subsidiaries. We used case study methodology to examine the transfer of knowledge practices within four MNCs in order to identify specific mechanisms through which social context impacts efficiency and effectiveness of transfer, as well as the resulting development of associated capabilities. The primary findings of this study are the identification of mechanisms that headquarters can use to create mutual interdependencies among subsidiaries, which in turn hasten capability development and enhance knowledge flow; and the mutually constructive roles of knowledge transfer and shared context, through promotion of organizational knowledge use. The manner in which these coordination and control mechanisms are implemented facilitates the integration of different units within the networked MNC.

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)0.019
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.050
GPT teacher head0.320
Teacher spread0.270 · 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