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Record W2164352601 · doi:10.5539/ass.v11n23p96

Mediating Risk Taking on Relationship between Board Structure Determinants and Banks Financial Performance

2015· article· en· W2164352601 on OpenAlexvenueno aff
Fazel Mohammadi Nodeh, Melati Ahmad Anuar, Suresh Ramakrishnan, Ali Akbar Rafatnia, Adel Mohammadi Nodeh

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOrdinary least squaresGeneralized method of momentsStructural equation modelingVariablesIndependence (probability theory)Sobel testAccountingEconometricsBusinessEconomicsStatisticsPanel dataMathematicsPath analysis (statistics)

Abstract

fetched live from OpenAlex

This study examines the relevance of bank board structure determinants (board independence, board size and concentrated ownership) on financial performance and level risk taking. In addition, the role of risk taking investigated as mediator variable on relationship between these variables and financial performance. This research contributes to the empirical research, Using a sample of 37 Malaysian Islamic and conventional banks over 2005-2014. In This study, The relationship between board structure determinants with level of risk taking and financial performance tested by pooled ordinary least square (OLS), fixed effects model, and generalized method of moments (GMM), however the role of risk taking as mediator variable examined by Baron and Kenny approach and Sobel test. The results shows that board structure have positive and negative relationship with financial performance and level of risk taking respectively. Furthermore, the relationship between board independence and concentrated ownership with financial performance mediate by risk taking.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.029
GPT teacher head0.264
Teacher spread0.235 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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