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Record W4383226874 · doi:10.1111/joms.12973

Business Groups and Export Performance: The Role of Coordination Failures and Institutional Configurations

2023· article· en· W4383226874 on OpenAlexaff
Daniel Shapiro, Saul Estrin, Michael Carney, Zhixiang Liang

Bibliographic record

VenueJournal of Management Studies · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsYork UniversityConcordia UniversitySimon Fraser University
FundersEconomic and Social Research Council
KeywordsInternationalizationTobit modelContext (archaeology)AutocracyEmerging marketsPoliticsBusinessSample (material)DemocracyIndustrial organizationEconomic systemEconomicsInternational tradeFinancePolitical science

Abstract

fetched live from OpenAlex

Abstract We explore the nature of business groups (BGs) and their affiliates in emerging markets through the lens of the coordination failures associated with economic development. We propose that BGs develop distinct economic and political capabilities that provide affiliates with access to the complementary resources required for successful exporting. We further argue that these capabilities are context‐specific, based on the market and political institutions of the home country. We propose that the BG advantage in supporting affiliate exporting increases as market institutions strengthen but is reduced (strengthened) as political systems become more democratic (autocratic). We apply Tobit estimation methods to a large sample of firms from emerging and developing countries at different stages of institutional development and find consistent evidence in favour of our hypotheses. We develop a framework to analyse alternative BG internationalization paths in a comparative institutional context.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.020
GPT teacher head0.244
Teacher spread0.224 · 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

Citations11
Published2023
Admission routes1
Has abstractyes

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