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Record W3094071640 · doi:10.5267/j.ac.2020.10.005

The effects of accounting benefit, ERP system quality and management commitment on accountants’ satisfaction

2020· article· en· W3094071640 on OpenAlexvenueno aff
Quỳnh Trang Nguyễn, Le Dinh Hoang Vu

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEnterprise resource planningVietnameseBusinessAccounting information systemQuality (philosophy)Management accountingAccountingKnowledge managementCompetitive advantageProcess managementMarketingComputer science

Abstract

fetched live from OpenAlex

Enterprise Resource Planning (ERP) systems are the largest software packages of information systems adopted by many organizations all over the world. The utilization of ERP systems has been considered as a key determinant of competitive advantages. The main objective of the present study is to propose a model of the accountant’s satisfaction in using enterprise ERP systems. Data were collected by using a questionnaire survey of almost 225 accountants working from 42 large Vietnamese construction firms. The results show that accounting benefits, ERP system quality and management commitment had significant effects on accountant’s satisfaction in the ERP environment. The study also indicate that the management commitment had an impact on ERP system quality and ERP system quality had a positive relationship with accounting benefits as well. Based on these findings, implications for managers in Vietnamese construction firms in providing accounting information quality are discussed.

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)0.007
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.034
GPT teacher head0.288
Teacher spread0.254 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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