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Record W4321508716 · doi:10.1108/mbr-10-2016-0037

Business group prevalence and impact across countries and over time

2017· article· en· W4321508716 on OpenAlexaff
Michael Carney, Marc van Essen, Saul Estrin, Daniel Shapiro

Bibliographic record

VenueMultinational Business Review · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsSimon Fraser UniversityConcordia University
Fundersnot available
KeywordsOriginalityPerspective (graphical)Foreign direct investmentValue (mathematics)EconomicsPsychologyMacroeconomicsSocial psychology

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine two prominent perspectives on business group functioning, institutional void (IV) and entrenchment/exploitation (EE), that make different predictions about the effect of business group (BG) on the economy. The authors examine the effects of BG prevalence in an economy and its effect on macroeconomic outcomes including foreign direct inward and outward investment, innovation and development of the financial sector. Design/methodology/approach The authors build a unique database by extracting estimates of BG prevalence for multiple countries between 1978 and 2012 from the existing literature and use this to test conflicting predictions derived from the IV and EE perspectives, respectively. Findings The authors find no consistent evidence that BG prevalence diminishes over time with economic development as IVs diminish, which is predicted by the IV perspective. Instead, the long-term persistence of BGs in many countries appears to be more consistent with the EE perspective. However, this study also finds no support for the perspective that high levels of BG prevalence are negatively associated with country-level indicators and determinants of economic development and competitiveness, as suggested by that perspective. Originality/value The authors conclude that there is no robust support for either the IV or the EE perspective and highlight the need for more contextualized theorizing about the evolution of BGs.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.125
Threshold uncertainty score0.875

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
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 head0.020
GPT teacher head0.289
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

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

Citations26
Published2017
Admission routes1
Has abstractyes

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