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Record W2773040367 · doi:10.5430/bmr.v6n4p64

Ubuntu or Botho African Culture and Corporate Governance: A Case for Diversity in Corporate Boards

2017· article· en· W2773040367 on OpenAlexvenueno aff
Tebogo Israel Teddy Magang, Veronica Goitsemang Magang

Bibliographic record

VenueBusiness and Management Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAutocracyOrganizational cultureNationalityDiversity (politics)Best practiceAccountingImpunityHofstede's cultural dimensions theoryCompliance (psychology)Ethnic groupSociologyBusinessPublic relationsPolitical scienceDemocracyEconomicsManagementLawSocial scienceHuman rightsPsychologySocial psychologyPolitics

Abstract

fetched live from OpenAlex

This paper aims to provide a theoretical analysis on the relationship between nationality/ethnicity and compliance with international best practice corporate governance principles. Using Hofstede-Gray cultural-accounting dimensions, the paper attempts to demonstrate that the Ubuntu/Botho culture may in some instances promote/not promote compliance with international best practice corporate governance principles because of the value system(s) of this culture. In view of this, the paper further attempts to present a case for diversity in corporate boards and executive management to enhance corporate compliance with best practice corporate governance principles, performance, disclosure etc. in line with the literature and theoretical arguments on diversity.On one hand, this paper provides future research an opportunity to empirically assess the relationship between corporate compliance with international best practice and nationality/ethnicity (Ubuntu/Botho culture). Future research could also investigate whether the Ubuntu/Botho values hold true today in view of the autocratic regimes in the African continent which have perfected a culture of impunity, corruption and bad governance.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.455
GPT teacher head0.408
Teacher spread0.047 · 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.

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

Citations1
Published2017
Admission routes1
Has abstractyes

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