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Record W2982901988

THE EFFECT OF STANDARDS OF ETHICAL CONDUCTION FOR MANAGEMENT ACCOUNTANTS ON FINANCIAL REPORTING QUALITY

2019· article· en· W2982901988 on OpenAlexvenueno aff
Tarawneh As, Altarawneh Ga

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAccountingCredibilityConfidentialityBusinessFinancial accountingCompetence (human resources)Quality (philosophy)Accounting information systemEconomicsComputer sciencePolitical scienceManagementComputer security
DOInot available

Abstract

fetched live from OpenAlex

This study aims to investigate the impact of the standards and the ethics of professional conduct of the managerial accountant on the quality of the financial reports. These standards embodied in competence, confidentiality, integrity, and credibility. To achieve the objective of the study a questionnaire was built and distributed to the sample of the study where the study population comprised Extractive and Mining industries enlisted in Amman Stock Exchange (ASE). In order to test the hypothesis, Stepwise Multiple Regression was used. The results have demonstrated that each of competence, confidentiality, and credibility has an impact on the financial reporting quality whereas there is no significant impact of the standard of integrity in the financial reporting quality. In light of the results of the study, the researchers recommend the need to emphasize the role of the standards of ethical conduct of the managerial accountant in achieving the quality of accounting information included in the internal financial reports. As well as the compatibility between the freedom of the managerial accountant to adhere to binding accounting standards and his commitment to the standards of ethical conduct while establishing an organizational mechanism to guide and monitor the compliance with these standards.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.186

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 science0.0000.000
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 head0.021
GPT teacher head0.322
Teacher spread0.301 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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