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Record W4409352128 · doi:10.1016/j.jwb.2025.101647

Towards a dynamic theory of lateral collaboration across multinational enterprise structures: A collection of insights

2025· article· en· W4409352128 on OpenAlexaff
Andreas Schotter, Martha L. Maznevski, Yves Doz

Bibliographic record

VenueJournal of World Business · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsWestern University
Fundersnot available
KeywordsMultinational corporationDynamic capabilitiesKnowledge managementBusinessEconomic geographyIndustrial organizationComputer scienceEconomics

Abstract

fetched live from OpenAlex

Achieving lateral collaboration benefits across multinational enterprise structures is elusive. Especially amid increasing international environmental complexities, prior research suggests that less hierarchical and more agile global collaboration arrangements are critical for organizational success. Drawing on extant work and six essays in this collection of recently published papers, we advance theory by explaining the relationships between socio-structural mechanisms, social systems, and individual actors, including boundary spanners. These relationships collectively drive commitments to lateral interdependence across intra-organizational boundaries. We offer an integrative model and guidance for future research in identifying the reciprocal relationships between influences to advance scholarly understanding of lateral collaboration.

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 score0.713
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.009
GPT teacher head0.262
Teacher spread0.252 · 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

Citations10
Published2025
Admission routes1
Has abstractyes

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