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Record W2979415593 · doi:10.4236/tel.2019.97157

International Diversification, Excessive Growth, and Corporate Governance

2019· article· en· W2979415593 on OpenAlexaff
Mengyang Guo, Justin Yiqiang Jin, Mary Li Zhi

Bibliographic record

VenueTheoretical Economics Letters · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDiversification (marketing strategy)Corporate governanceBusinessEarningsEarnings growthCapital (architecture)External financingAdverse selectionFinanceMonetary economicsEconomicsAccountingMarketing

Abstract

fetched live from OpenAlex

This study examines the relationship between international diversification and firms’ access to external capital to finance growth opportunities. We hypothesize that moral hazards, adverse selection, and home bias arise when firms expand across borders. These problems may hinder the portion of firm growth that is financed by external capital providers known to play a monitoring role. Using various measures of firms’ excessive growth and international diversification, we show that external capital providers do not view the international expansion of operations as value-enhancing activities. We also find that efforts of corporate governance (e.g., through higher levels of corporate governance and the disclosure of segment earnings) can be an effective strategy to alleviate external capital providers’ concerns and achieve higher growth rates through the expansion of international operations.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.167
Teacher spread0.159 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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