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Record W4287307282 · doi:10.1002/jcaf.22582

Exploring the effects of family control on dividend policy—Evidence from Canada

2022· article· en· W4287307282 on OpenAlexaffabout
Raymond Leung

Bibliographic record

VenueJournal of Corporate Accounting & Finance · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsDividendEarningsShareholderDividend policyBusinessCashAgency (philosophy)Control (management)Monetary economicsEconomicsCorporate governanceAccountingFinance

Abstract

fetched live from OpenAlex

Abstract This study examines if publicly listed family‐controlled corporations (hereafter called FCCs) have more differential effects on cash dividend policy (in both willingness to pay and amounts paid) than non‐FCCs. While the literature provides mixed conclusions, this study focuses on the theoretical underpinning that FCCs’ unique behavior model with objective to preserve their socio‐emotional wealth (SEW) tend to protect their highly undiversified invested capital, operate conservatively and conserve corporate wealth for future generations. As a result, FCCs, especially those with large size and large accumulated profits, would pursue type II expropriative agency motive by lowering cash dividend payout. Nevertheless, when FCCs operate for long years, their transgenerational sustainability intentions and reputational concerns stemmed from the SEW will induce FCCs to endeavor non‐financial goals. This study further finds evidence that FCCs with longer years, and accumulated large retained earnings and size are willing to align with minority shareholders’ interests by being more willing to pay more cash dividends. However, comparing to their non‐FCC counterparts, the heterogeneous variable of FCCs age interacts with retained earnings and size to cause larger positive effects in willingness to pay more cash dividends; and cash dividend payouts.

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)0.001
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 score0.187
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.053
GPT teacher head0.219
Teacher spread0.166 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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