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Record W3169359558 · doi:10.2478/sbe-2021-0017

The Corporate Governance Models for Banks: A Comparative Study

2021· article· en· W3169359558 on OpenAlexaff
Salah U‐Din

Bibliographic record

VenueStudies in Business and Economics · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsMount Royal University
Fundersnot available
KeywordsCorporate governanceBusinessAccountingStakeholderShareholderMargin (machine learning)Financial systemEconomicsFinance

Abstract

fetched live from OpenAlex

Abstract An effective corporate governance system is vital in modern-day economics and firms. It can help to specify the distribution of roles, responsibilities, and resources among various stakeholders of an organization or society. The enhanced role of the banks in various economic systems demands a higher level of corporate bank governance for a stable and sustainable financial system. In this paper; four major corporate governance models of banks are compared and the financial outcomes of each model are analyzed to assess their alignment with expectations of an effective corporate governance system. The Continental corporate governance model found to be closer to the expectations of an effective corporate governance system compared to the Anglo- Saxon, Chinese, and Islamic banking. Banks under the Continental model charge lower margin to its customers, use bank resources more efficiently and create relative balance in the distribution of resources among all stakeholders compared to the other three models. Banks under the Anglo- Saxon model are charging higher margin to its customer, Chinese banks are under-utilizing the banks’ resources, and Islamic banks are more favoring their shareholders and are riskier among banks of all selected models. Higher involvement of the more stakeholders in the decision-making process of the banks is key to effective corporate governance and sustainable banking system. Reforms in all corporate governance models are recommended while keeping in mind the prior research on corporate governance especially the Sir Adrian Cadbury report.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.104
GPT teacher head0.275
Teacher spread0.171 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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