MétaCan
Menu
Back to cohort
Record W2911681598 · doi:10.5430/afr.v8n1p128

Influence of Management Commitment and Organizational Structure on Application of ERP System & Its Impact on Quality of Accounting Information: A Survey in Vietnamese Telecommunication Enterprises

2019· article· en· W2911681598 on OpenAlexvenueno aff
Nguyen Thanh Hung, Tran Thi Hong, Nguyen Le Duc

Bibliographic record

VenueAccounting and Finance Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsVietnameseBusinessKnowledge managementStructural equation modelingEnterprise resource planningAccounting information systemQuestionnaireSample (material)Order (exchange)Quality (philosophy)Organizational structureManagement accountingSurvey data collectionProcess managementAccountingComputer scienceManagementFinanceEconomics

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the influence of management commitment and organizational structure to the application of the enterprise resource planning (ERP) system and its implications on the quality of the accounting information at Vietnamese Telecommunication enterprises (VTEs). In order to achieve the goal of this study, the researcher chooses to survey VTEs that uses ERP systems. A questionnaire was designed and distributed to a sample of accountants and financial managers who works at such companies. A survey with 286 usable questionnaire responses is used to test the research hypotheses. The results show that both management commitment and organizational structure affect the level of ERP application, in which management commitment is the most influential factor. In addition, the ERP application also contributes to improving the quality of accounting information. The research model used is the structure equation model (SEM).

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.370
Teacher spread0.327 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAccounting and Finance ResearchSame topicERP Systems Implementation and ImpactFrench-language works237,207