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Record W4416986912 · doi:10.1177/09713557251398993

Family Firms and CSR Composition: Internal Versus External Practices in Latin America

2025· article· en· W4416986912 on OpenAlexaff
Cris Bravo Monge

Bibliographic record

VenueThe Journal of Entrepreneurship · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLatin AmericansCorporate social responsibilitySocioemotional selectivity theoryLegitimacyCorporate governanceEmerging markets

Abstract

fetched live from OpenAlex

Corporate social responsibility (CSR) is central to debates on the legitimacy and competitiveness of family firms, yet evidence on ownership effects remains inconsistent. While socioemotional wealth perspectives highlight reputational motives, capability-based views suggest that resource constraints may limit substantive internal investments. Most prior studies focus on aggregate CSR levels and on Europe or North America, leaving unanswered whether ownership shapes the composition of CSR activities in under-represented contexts such as Latin America. This article examines 315 listed firms in Argentina, Brazil, Chile, Colombia and Mexico between 2019 and 2023. Using environmental, social and governance ratings and generalised linear models with size, age, country and sector controls, this study tests whether family ownership predicts internal versus external CSR outcomes. The authors find that non-family firms outperform in capability-intensive internal CSR, while external CSR and governance show parity. These results highlight a visibility–capability trade-off and suggest that Latin American family firms must enhance their operational capabilities to address CSR gaps.

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)0.002
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 score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.037
GPT teacher head0.286
Teacher spread0.248 · 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
Published2025
Admission routes1
Has abstractyes

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