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Record W1979967730 · doi:10.5539/ass.v10n12p6

The Moderating Effect of Board Diversity on the Relationship between Executive Committee Characteristics and Firm Performance in Oman: Empirical Study

2014· article· en· W1979967730 on OpenAlexvenueno aff
Ebrahim Mohammed Al‐Matari, Abdullah Kaid Al‐Swidi, Faudziah Hanim Fadzil

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsModerationAccountingMultilevel modelContext (archaeology)Diversity (politics)Executive boardPsychologyGender diversityExecutive committeeExecutive summaryAssociation (psychology)BusinessCorporate governanceManagementSocial psychologyPolitical scienceEconomicsStatisticsFinanceLaw

Abstract

fetched live from OpenAlex

This study focuses to achieve an important objective by examining the moderating effect of board diversity (foreign member on the executive committee and executive committee commitment) on the relationship between executive committee characteristics and firm performance in Omani companies excluding those categorized under the financial sector. The sampling covers two years, 2011 and 2012. This study used multiple regression and hierarchical multiple regression to analyze the association between independent, moderating and dependent variables. Based on the findings, a positive association between executive committee independence, executive committee meeting and firm performance is revealed although it is not significant. In the same context, the finding revealed a negative relationship between executive committee size and firm performance but not significant. Moreover, the board diversity moderated the relationship between executive committee characteristics and firm performance but the effect is not significant. Finally, this study offers recommendations for future researchers at the end.

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)0.001
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 score0.025
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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

Citations28
Published2014
Admission routes1
Has abstractyes

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