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Record W2058520045 · doi:10.5430/rwe.v6n1p85

Management Styles and Organisational Effectiveness in Vietnam: A Comparison in Terms of Management Practices between State-owned and Foreign Enterprises

2015· article· en· W2058520045 on OpenAlexvenueno aff
Lai-Wang Wang, Quoc-Liem Le, Thanh Tuyen Tran

Bibliographic record

VenueResearch in World Economy · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessHuman capitalVietnameseSample (material)Exploratory factor analysisOrder (exchange)Human resourcesHuman resource managementPerformance appraisalQuality (philosophy)MarketingKnowledge managementIndustrial organizationEconomicsFinanceManagementComputer scienceEconomic growth

Abstract

fetched live from OpenAlex

Human capital initiatives play a vital role in resolving the most pressing business challenges. The effective management of human poses new challenges in the organization for leadership and managerial capabilities. The current study aims to analyze the factors that are closely associated with employee satisfaction in both Vietnamese state-owned enterprise and foreign-owned enterprise in order to understand the difference of management practices. The response was taken from a sample of 250 respondents in either two types of business and the data are carried out by using multiple statistical analyses, including exploratory factor analysis, reliability test, mean point value, and multiple linear regressions. The results indicate that for the state-owned enterprise, recruitment process is the most important factor that helps to improve the satisfaction of employee. Besides, for the foreign-owned enterprise, as the results of the current study, performance appraisal process is the main point to solve employee‘s problems. The findings also provide useful implications for managers in terms of improving human capital quality, and stabilizing the labor resources and reducing the turnover rate for both state-owned enterprise and foreign-owned enterprises.

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)0.004
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 score0.177
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
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 head0.155
GPT teacher head0.364
Teacher spread0.209 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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