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Record W4300594216 · doi:10.46697/001c.38486

General Manager Succession in Multinational Enterprise Subsidiaries

2022· article· en· W4300594216 on OpenAlex
Liang Li

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

VenueAIB Insights · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSuccessor cardinalSubsidiaryMultinational corporationEcological successionNationalityBusinessProcess (computing)Industrial organizationManagementEconomic geographyPolitical scienceComputer scienceEconomicsFinanceMathematicsEcology

Abstract

fetched live from OpenAlex

This dissertation, based on interviews with over 40 managers and quantitative data on over 1,900 foreign subsidiaries, aims to provide new insights regarding subsidiary general manager (GM) changes in multinational enterprises (MNEs). I study continual GM change, individual succession event, the decision-making process through which different types of subsidiary GM successors are selected, and how they link to subsidiary performance. I found that GM succession decisions are path dependent and evolving, can be influenced by MNE environments, and need to be based on both GM successor nationality and origin. My dissertation underscores the complexity of subsidiary GM succession and bridges succession strategy with implementation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.765
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
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 head0.011
GPT teacher head0.221
Teacher spread0.211 · 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