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Record W3122145529 · doi:10.5539/mas.v15n1p143

The Impact of Information Technology Governance in Reducing Cloud Accounting Information Systems Risks in Telecommunications Companies in the State of Kuwait

2021· article· en· W3122145529 on OpenAlexvenueno aff
Mohammad Z. M. Alotaibi, Mohammad F. E. Alotibi, Omar Zraqat

Bibliographic record

VenueModern Applied Science · 2021
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceAccounting information systemCloud computingAccountingBusinessInformation systemInformation technologyService (business)PopulationInformation and Communications TechnologyTelecommunicationsComputer scienceMarketingFinanceEngineering

Abstract

fetched live from OpenAlex

This study aims to identify the impact of information technology governance in reducing cloud accounting information systems Risks in Kuwaiti telecommunications companies. The study population represented by all Kuwaiti telecommunications companies, which number (3) companies. The sampling unit consisted of workers in the upper and middle management of Kuwaiti telecommunications companies. The researcher distributed (327) questionnaires electronically, the researcher retrieved (291) questionnaires, of which (269) were valid for statistical analysis. The results indicated that the relative importance of all dimensions of information technology governance. The results demonstrate the importance of the role of information technology governance in reducing cloud accounting information systems risks. Also, that all information technology governance dimensions (Align, Plan and Organize, Build, Acquire and Implement, Deliver, Service and Support, Monitor, Evaluate and Assess) affect the cloud accounting information systems risks reduction in Kuwaiti telecommunications companies.

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)0.004
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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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