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Record W1837253609 · doi:10.5539/res.v7n11p359

Key Determinants of SMEs in Vietnam. Combining Quantitative and Qualitative Studies

2015· article· en· W1837253609 on OpenAlexvenueno aff
Uyen H. P. Phan, Phuong V. Nguyen, Thao Phuong Le

Bibliographic record

VenueReview of European Studies · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsViet namVietnameseBusinessContext (archaeology)Christian ministryPoint (geometry)Sustainable developmentChinaMarketingIndustrial organizationSmall and medium-sized enterprisesLoanFinanceEconomicsEconomy

Abstract

fetched live from OpenAlex

This paper aims to identify key determinants impacting on a firm performance of Small and Medium-sized Enterprises (SMEs) in Viet Nam. SMEs have contributed significantly to the overall Vietnamese economy. However, in the context of emerging market in Viet Nam nowadays, SMEs have to deal with a tough competitive market. Hence, an awareness what factors enhancing firm performance enables them to achieve sustainable development. Based on the survey of 2551 Viet Nam SMEs, which was conducted by Science & Social Association, Viet Nam Ministry of Labor- Invalids and Social Affairs, the paper provides the holistic view of several perspectives’ impacts on the performance of SMEs. We find out that three main factors, including human resource, education level of entrepreneurs and training cost have significant effects on the performance. As considering in the field of international trade, the results illustrate the more SMEs conduct export activities, the better enterprises’ performance. Specifically, we point out that other factors such as formalization, credit access, informal loan and firm location are strongly associated with the performance. Furthermore, we also conducted in-depth interviews with 6 entrepreneurs and top managers to explore current challenges of Vietnamese SMEs and suggest appropriate solutions to overcome them.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.458
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.218
GPT teacher head0.425
Teacher spread0.207 · 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 designQualitative
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
Published2015
Admission routes1
Has abstractyes

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