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Record W3130224539 · doi:10.5267/j.msl.2021.1.016

Factors affecting financial management: Case study of educational manager training and fostering public institutions

2021· article· en· W3130224539 on OpenAlexvenueno aff
Thang Quyet Nguyen, Ha Thanh Viet, Le Thi Thanh Loan

Bibliographic record

VenueManagement Science Letters · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicResearch studies in Vietnam
Canadian institutionsnot available
Fundersnot available
KeywordsCronbach's alphaBusinessFinancial managementRestructuringExploratory factor analysisExploratory researchHo chi minhFinanceService (business)MarketingEconomicsSocioeconomics

Abstract

fetched live from OpenAlex

The research focuses deeply on and evaluates the factors affecting financial management of educational managers training and fostering institutions in Ho Chi Minh City, Vietnam. This is a very specific model for countries in transition economies as in Vietnam, a country is in the process of comprehensive renovation, restructuring public service delivery institutions, increasing the assignment of self-responsibility to institutions, reducing financial pressure on the State budget. Using qualitative and quantitative research methods together with techniques, i.e., testing reliability scales with Cronbach's alpha coefficients, exploratory factor analysis EFA, CFA and linear model SEM, the study investigated 500 samples in 07 educational institutions in Ho Chi Minh City, Vietnam. The findings revealed six (06) factors, which are internal control system, technology infrastructure, top managers’ commitment, cash management and budget system, organizational responsibility, affected the financial reporting system, and meanwhile, the financial reporting system has a positive impact on the financial management. Based on this result, the study has proposed implications for improving financial management in educational managers training and fostering institutions in Ho Chi Minh City, Vietnam.

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)0.001
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 score0.111
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.003
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 head0.096
GPT teacher head0.330
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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

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