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Record W4408256151 · doi:10.5267/j.dsl.2025.1.009

The effect of strategic audit on improving financial performance and risk management: Field study on Sudanese banks

2025· article· en· W4408256151 on OpenAlexvenueno aff
Mohamed Ali, Amina Abdelgadir Ali Humeida, Omer Tajelsir Omer Elnour, Abdelmjeed Abdelrahim Ali Alajab, JR Ali

Bibliographic record

VenueDecision Science Letters · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsnot available
FundersKennesaw State UniversityPrince Sattam bin Abdulaziz University
KeywordsAuditBusinessField (mathematics)Risk managementFinancial AuditStrategic managementInternal auditAccountingFinanceRisk analysis (engineering)Marketing

Abstract

fetched live from OpenAlex

The study's objective is to verify the effects of strategic review on the financial performance and risk management of banks in PortSudan City- Sudan. The descriptive analytical approach was used to accomplish the study's goals. By designing and distributing 180 questionnaires, of which 170 were collected. They were analyzed using path analysis using the partial squares technique. The main results indicated a positive effect of a strategic review on the financial performance of Sudanese banks. It also showed the positive effects of a strategic review on the risk management of Sudanese banks. The value of these results is that improved financial performance will make financial reports more reliable and trustworthy; therefore, it may attract more funds from the public. Investors and other stakeholders are interested in the bank's financial position and expected future operating results. They will use this information to prepare risk reports or make important business decisions. Therefore, if external decision-makers provide a reliable positive return, improving risk management will also contribute positively to shareholder value.

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)0.007
metaresearch head score (Gemma)0.010
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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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