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Record W3159738588

On the Polylithic Nature of Family-Owned Businesses: Heterogeneous Responses to – and from – Brazilian Financial Market Reform

2020· article· en· W3159738588 on OpenAlexaff
Susan Perkins, Edward J. Zajac

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsCorporate governanceFinancial marketBusinessEmerging marketsTypologyGlobalizationStock marketAccountingMarket economyFinanceEconomics
DOInot available

Abstract

fetched live from OpenAlex

Accompanying the increasing globalization of firms and financial markets has been the growing belief that both firms and their financial markets will benefit from the adoption of improved corporate governance practices, particularly in emerging markets historically dominated by family-owned firms. We provide a more nuanced examination of this belief by advancing a theoretical perspective on the polylithic nature of family-owned firms, and use our typology of family firms to hypothesize why some family firms are more open to improved corporate governance than others. We contextualize our hypotheses using the recent Brazilian financial market reform, which allowed firms to self-select into new trading sub-segments distinguished by the stringency of corporate governance requirements. We offer and test differentiated predictions as to which type of Brazilian family-owned firms is most/least likely to self-select into more stringent trading segments, and with what financial market consequences. We predict and find that Brazilian financial markets do generally reward firms that choose a more stringent stock market segment, but do not anticipate that some firms engage in symbolic vs. substantive adoption of good governance practices. We discuss the implications of our approach and findings for research on corporate governance, family-owned firms, and institutional change in emerging markets.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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