MétaCan
Menu
Back to cohort
Record W3131653137 · doi:10.5267/j.ac.2021.1.021

Cloud accounting information systems: Threats and advantages

2021· article· en· W3131653137 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology in Education and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAuditOvertimeCloud computingAccountingAccounting information systemBusinessComputer securityConfidentialityComputer scienceEconomics

Abstract

fetched live from OpenAlex

When the Corona-virus pandemic hit the world, executing and performing the jobs of the remote world became an imperative, this pandemic became as motivator to create this study which aimed to reveal the advantages and threats that encounter the implementing of Cloud Accounting Information Systems CAIS from the view point of external auditors. For that purpose, a questionnaire was developed and distributed then from 198 valid questionnaires which were analyzed by a T- test. It has been found that the main advantages are a reduction of labor and overtime costs because of the ability to access the system from anywhere, while penetration, interruption, and confidentiality are the main threats, also it was found that there is a strong relationship between implementing cloud accounting information systems (CAIS) and the limiting manipulation of financial information. The study therefore recommended upgrading the security procedures of cloud accounting systems and holding courses for auditors to enhance their abilities in auditing CAIS.

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
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 head0.008
GPT teacher head0.245
Teacher spread0.238 · 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