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Record W2000328018 · doi:10.5539/ibr.v6n6p181

Electronic Audit Role in Achieving Competitive Advantages and Support the Strategy of the External Audit in Auditing Offices in the Hashemite Kingdom of Jordan

2013· article· en· W2000328018 on OpenAlexvenueno aff
Reem Okab

Bibliographic record

VenueInternational Business Research · 2013
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditBusinessQuality auditExternal auditorCompetitive advantageInformation technology auditAccountingJoint auditPerformance auditAudit planFlexibility (engineering)Market shareInternal auditMarketingEconomicsManagement

Abstract

fetched live from OpenAlex

The study aimed at identifying the role of electronic audit in achieving the competitive advantages and support the strategy of support the strategy of external audit used by audit’s offices in Jordan by testing a set of hypotheses addressing the role of electronic audit in achieving competitive advantage’s dimensions in audit’s offices and support the strategy of audit. The study identified the obstacles that hinder the benefit of using electronic audit which aims at achieving the competitive advantages and support audit strategy followed by audit’s office in the Hashemite Kingdom of Jordan. The study found that using electronic audit contributes to achieve the competitive advantages in Jordan including cost reduction, quality, flexibility, market share. Using electronic audit also contributes to support the strategy of external audit. And the study indicated that there are the obstacles hindering using electronic audit including the cost of specialized audit program, increase of general programs’ prices, and their lack of suitability for all work establishments in addition to a necessity of scientific and practical qualification of auditor who specialized in information technology.

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)0.000
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 score0.081
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.308
Teacher spread0.285 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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