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Record W4395004690 · doi:10.5430/afr.v13n2p63

Factors Influencing the Integration of Cloud Computing in Modern Accounting Practices in the Malaysian Accounting Sector: A Conceptual Study

2024· article· en· W4395004690 on OpenAlexvenueno aff
Mohd Fairuz Adnan, Sarah Amalin Zawari, Saleh Hashim

Bibliographic record

VenueAccounting and Finance Research · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsCloud computingAccountingBusinessConceptual modelAccounting information systemConceptual frameworkManagement accountingEconomicsComputer scienceSociologyDatabase

Abstract

fetched live from OpenAlex

In the dynamic landscape of contemporary business, cloud computing has become a prevalent tool for organizations to manage and store data efficiently as more organizations adopt cloud-based solutions for their IT needs. In this case, the adoption of cloud computing is beneficial not only for managing and storing data but also for implementing effective cloud accounting systems. However, amidst the transformation and benefits, adopting and integrating cloud computing in accounting practices are not without challenges. This study aims to delve deeper into these issues to investigate the key factors influencing the adoption of cloud computing in accounting, particularly in the Malaysian business landscape. The factors explore the distinctive benefits like security, cost-effectiveness and flexibility while also shedding light on the associated challenges, with a particular emphasis on security. Despite these challenges, such as security vulnerabilities, cost overruns and potential for data loss, the study asserts that the benefits of integrating cloud computing into accounting practices substantially outweigh the hurdles. Consequently, it recommends strategic steps to ensure a smooth transition to cloud-based accounting systems. These measures are critical in aiding businesses in navigating through the challenges while capitalizing on the transformative potential of cloud computing, thereby staying competitive and agile in the rapidly evolving digital era.

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)0.010
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0000.002
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 head0.132
GPT teacher head0.367
Teacher spread0.235 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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