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Record W2744893867 · doi:10.5539/ijef.v9n9p40

Agency Conflicts, Socioemotional Wealth, and the Debt Maturity Structure of Family Firms: A Critical Analysis

2017· article· en· W2744893867 on OpenAlexvenueno aff
Oscar Domenichelli

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsnot available
Fundersnot available
KeywordsSocioemotional selectivity theoryShareholderAgency (philosophy)DebtMaturity (psychological)CreditorBusinessFlourishingPublic economicsEconomicsCorporate governanceFinanceSociologySocial psychologyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

This paper aims to study the impact of the distinctive agency and socioemotional features of family firms on their debt maturity choices using a literature analysis of this topic, still substantially unexplored. Therefore, the paper examines the relationships between owners and managers; majority and minority shareholders, and family shareholders and family outsiders; and owners and creditors. The analysis suggests that the propensity of family businesses to use long-term debt depends on the generation leading the family firm, family blockholders, motivation for expropriating minority shareholders, family outsiders and their socioemotional orientation. Much still remains to be empirically studied. One interesting issue to explore further would be the influence of country-specific factors worldwide, in combination with firm-specific characteristics relating to agency conflicts and socioemotional wealth, on the debt maturity decisions of family firms, compared to non-family ones. Given the international importance of family firms, and their widespread presence and activity worldwide, additional empirical results on this topic may help governments adopt specific policies, that will better support family businesses, in light of their peculiar and unique economic and non-economic aspects.

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)0.000
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 score0.732
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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
Published2017
Admission routes1
Has abstractyes

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