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Record W3154617758 · doi:10.5267/j.ac.2021.3.023

The effect of the human resources cycle in improving the effectiveness of the accounting information system in Jordanian banks

2021· article· en· W3154617758 on OpenAlexvenueno aff
Ibraheem Jodeh

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSolvencyBusinessMarket liquidityAccountingAccounting information systemHuman resourcesProfit (economics)FinanceEconomicsManagement

Abstract

fetched live from OpenAlex

This research aims to study the effect of the human resources cycle in improving the effectiveness of the accounting information system in Jordanian banks by measuring the effect of the number of employees, total training expenditures, the total of salaries and rewarding workers and the total expenditures of workers in Jordanian banks on improving the effectiveness of the accounting information system in Jordanian banks through the net profit after tax deduction for the years from 2010 to 2019. The results of the study indicate that there is a statistically significant effect at the level of confidence of 5% for the number of employees, total training expenditures, and total employee expenditures on improving the effectiveness of the information system in Jordanian banks and the absence of a statistically significant relationship to the effect of total salaries and remunerations of workers, as the parameter was -5.934. The study recommended measuring the effect of the human resources cycle in improving the effectiveness of the information system in Jordanian banks in terms of liquidity and solvency, and in other business sectors.

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)0.001
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 score0.017
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.003
GPT teacher head0.185
Teacher spread0.182 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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