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Record W2961097781 · doi:10.5267/j.msl.2019.7.002

Factors affecting the application of management accounting in small and medium enterprises in Hanoi, Vietnam

2019· article· en· W2961097781 on OpenAlexvenueno aff
Thi Thuy Hong Nguyen, Thi Thanh Thuy Chu, Dinh Dong Nguyen, Thi To Phuong Nguyen

Bibliographic record

VenueManagement Science Letters · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessManagement accountingAccountingCompetition (biology)Quality (philosophy)Process (computing)Small and medium-sized enterprisesProduction (economics)Accounting information systemMarketingEconomicsFinance

Abstract

fetched live from OpenAlex

This article evaluates the factors affecting the use of management accounting in small and medium enterprises (SMEs) in Hanoi, Vietnam. The factors include: Production and business characteristics; Competitiveness; Business strategy; CEO of awareness management; Human resource quality. The study is conducted on 238 SMEs in Hanoi and the results show that CEO's awareness of management accounting had the strongest impact on the ability to apply management accounting of enterprises while competition level factor had the weakest effect. At the same time, the study also examines the significant role of mediating factor of firm size on the relationship between the characteristics of the production and business process and the application of management accounting, age and professional qualifications. The results indicate that enterprise size plays a significant role on regulating the impact of business characteristics of enterprises on management accounting application. For smaller enterprises, management accounting is less used and vice versa. Next, the younger the managers are, the higher the impact of their awareness on management accounting application, which means management accounting will be used more if managers are younger.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.201
Teacher spread0.194 · 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.

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

Citations15
Published2019
Admission routes1
Has abstractyes

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