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Record W2282253560 · doi:10.5539/ibr.v9n2p137

The Impact of Applying the Electronic Cheque Clearing System on Employees’ Satisfaction in Accounting Departments’ of Jordanian Islamic Banks

2016· article· en· W2282253560 on OpenAlexvenueno aff
Adel M. Qatawneh, Fairouz M. Aldhmour, Lara T. Aldmour

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
FundersDivision of Electrical, Communications and Cyber Systems
KeywordsChequeClearingIslamDirectoryBusinessSample (material)AccountingWork (physics)MarketingFinanceEngineeringComputer science

Abstract

fetched live from OpenAlex

This study aims to investigate the impact of applying the electronic cheque clearing system on employees’ satisfaction in Accounting Departments’ of Jordanian Islamic banks. The population of this study was the employees who work at the Accounting Departments. A random sample of (150) employees who work in Accounting departments, particularly clearing directory in the Islamic Banks in Jordan were chosen to be the sample of the study. One hundred and fifty questionnaires were distributed to the employees who work in clearing directory and those who worked in this directory in the Islamic Banks in Jordan. The results indicate that there is a weak impact of the independent variable the applying ECC system (Reliability, Responsiveness, Tangibility and Privacy) on the dependent variable (Employees’ Satisfaction) in Jordanian Islamic banks. Against expectation, the results show that only Tangibility has a positive and significant relationship with ‘Employees’ Satisfaction. The researchers recommended that employees’ satisfaction in bank sector may affect customers’ satisfaction positively or negatively because most of employees in this sector deal with customers face to face.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.324
Teacher spread0.299 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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