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Record W4388315826 · doi:10.5267/j.uscm.2023.8.015

The moderating role of internal control system on the relationship between service quality of accounting information system and customer satisfaction: a study of some selected customers from commercial banks in Jordan

2023· article· en· W4388315826 on OpenAlexvenueno aff
Mohammad Haider Alibraheem, Ibrahim Mahmoud Siam, Khaleel Ibrahim Al-Daoud, Ahmad Y. A. Bani Ahmad, Khaled Adnan Bataineh, Mohammad Al Zoubi

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsCustomer satisfactionBusinessContext (archaeology)Control (management)Service qualityQuality (philosophy)Accounting information systemProcess managementInformation systemService (business)Empirical researchMarketingKnowledge managementAccountingComputer scienceEngineering

Abstract

fetched live from OpenAlex

The purpose of this empirical study is to determine how Jordan's internal control system affects the efficiency of accounting information systems (AIS) services and client satisfaction. The study's goal is to shed light on how important a robust internal control system is for increasing customer satisfaction with AIS services. A quantitative research methodology is used to collect data from a survey of 265 representative customers of Jordanian enterprises (Commercial BANKS). Data is analyzed using second generation analysis technic (SmartPLS) software. In the context of AIS, the findings emphasize the significance of the internal control system in bolstering the link between service quality and customer satisfaction. The research advances our understanding of AIS and has significant ramifications for companies seeking to improve customer satisfaction and service quality. The researcher also offered suggestions for further research.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.250
Teacher spread0.228 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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