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Record W4226354240 · doi:10.5267/j.ijdns.2022.2.013

Effectiveness of the board of directors' performance in Jordan: The moderating effect of enterprise risk management

2022· article· en· W4226354240 on OpenAlexvenueno aff
Saddam Ali Shatnawi, Ahmad Marei, Luay Daoud, Dina Alkhodary, Maha Shehadeh

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanking, Crisis Management, COVID-19 Impact
Canadian institutionsnot available
Fundersnot available
KeywordsReturn on assetsAccountingBusinessEnterprise risk managementStock exchangeReturn on equityRegression analysisModerationEquity (law)Risk managementMultilevel modelFinancePsychology

Abstract

fetched live from OpenAlex

This study aims to investigate the moderating effect of enterprise risk management on the relationship between the board of directors’ effectiveness on accounting and market performance in Jordan. The current study uses panel data of 684 firm-year observations, employed regression analysis and analysis of annual reports of 76 listed companies on the Amman stock exchange (ASE) from 2009 to 2017 covering 9 years. The findings of the hierarchical regression analysis showed that the enterprise risk management has a significant positive moderating effect on the relationship between the board of directors’ effectiveness on accounting and market performance in Jordan. The findings reveal that enterprise risk management positively moderated the relationship between board of directors’ effectiveness on Return on Assets, Return on Equity, and Tobin’s Q. It also moderated the interaction of board of directors’ effectiveness intercept enterprise risk management on Return on Assets and Return on Equity, which were found positive and significant. The findings of this paper can provide crucial conclusions and recommendations that clarify the relationship between the board of directors’ effectiveness and the accounting and market performance in Jordan and the moderate impact of the enterprise risk management.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.013
GPT teacher head0.272
Teacher spread0.260 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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